5 code implementations • 21 Sep 2022 • Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, Luke Zettlemoyer
The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences.
Ranked #1 on Long-range modeling on LRA
2 code implementations • ACL 2020 • Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. black, Shrimai Prabhumoye
This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning.
1 code implementation • ICLR 2021 • Hieu Pham, Xinyi Wang, Yiming Yang, Graham Neubig
Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data.
1 code implementation • 21 Dec 2022 • John Wieting, Jonathan H. Clark, William W. Cohen, Graham Neubig, Taylor Berg-Kirkpatrick
Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes or careful engineering to work well.
3 code implementations • 26 Feb 2022 • Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn
We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages.
4 code implementations • 15 Jan 2017 • Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, Pengcheng Yin
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
1 code implementation • 23 Aug 2023 • Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch, Tongshuang Wu, Graham Neubig
In this paper, we propose Prompt2Model, a general-purpose method that takes a natural language task description like the prompts provided to LLMs, and uses it to train a special-purpose model that is conducive to deployment.
1 code implementation • ICLR 2020 • Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen
In particular, we describe a neural module, DrKIT, that traverses textual data like a KB, softly following paths of relations between mentions of entities in the corpus.
2 code implementations • 18 Nov 2022 • Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, PengFei Liu, Yiming Yang, Jamie Callan, Graham Neubig
Much of this success can be attributed to prompting methods such as "chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem.
Ranked #17 on Arithmetic Reasoning on GSM8K
1 code implementation • NeurIPS 2023 • Amanda Bertsch, Uri Alon, Graham Neubig, Matthew R. Gormley
This kNN index can be kept on either the GPU or CPU memory and queried in sub-linear time; this way, we can index practically unlimited input sequences, while every attention head in every decoder layer retrieves its top-k keys, instead of attending to every key.
3 code implementations • 25 Jul 2023 • I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, PengFei Liu
With the above challenges in mind, in this paper, we propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models (e. g., ChatGPT).
1 code implementation • EMNLP 2020 • Sai Muralidhar Jayanthi, Danish Pruthi, Graham Neubig
We introduce NeuSpell, an open-source toolkit for spelling correction in English.
4 code implementations • 24 Mar 2020 • Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, Melvin Johnson
However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing.
2 code implementations • ICML 2020 • Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, Melvin Johnson
However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing.
Ranked #1 on Zero-Shot Cross-Lingual Transfer on XTREME (AVG metric)
1 code implementation • EMNLP 2021 • Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, PengFei Liu, Junjie Hu, Dan Garrette, Graham Neubig, Melvin Johnson
While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others.
1 code implementation • ACL 2020 • Pengcheng Yin, Graham Neubig, Wen-tau Yih, Sebastian Riedel
Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks.
Ranked #9 on Text-To-SQL on spider (Exact Match Accuracy (Dev) metric)
1 code implementation • 11 May 2023 • Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig
In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation.
1 code implementation • 25 Jul 2023 • Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, Graham Neubig
Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.
1 code implementation • 26 Feb 2020 • Xinjian Li, Siddharth Dalmia, Juncheng Li, Matthew Lee, Patrick Littell, Jiali Yao, Antonios Anastasopoulos, David R. Mortensen, Graham Neubig, Alan W. black, Florian Metze
Multilingual models can improve language processing, particularly for low resource situations, by sharing parameters across languages.
1 code implementation • ICLR 2022 • Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig
Furthermore, our unified framework enables the transfer of design elements across different approaches, and as a result we are able to instantiate new parameter-efficient fine-tuning methods that tune less parameters than previous methods while being more effective, achieving comparable results to fine-tuning all parameters on all four tasks.
7 code implementations • ACL 2018 • Pengcheng Yin, Chunting Zhou, Junxian He, Graham Neubig
Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.
4 code implementations • EMNLP 2018 • Pengcheng Yin, Graham Neubig
We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs).
Ranked #2 on Semantic Parsing on ATIS
2 code implementations • NAACL 2019 • Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, Xinyi Wang, John Wieting
In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation.
1 code implementation • WS 2019 • Xi-An Li, Paul Michel, Antonios Anastasopoulos, Yonatan Belinkov, Nadir Durrani, Orhan Firat, Philipp Koehn, Graham Neubig, Juan Pino, Hassan Sajjad
We share the findings of the first shared task on improving robustness of Machine Translation (MT).
3 code implementations • ACL 2018 • Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig, Eduard Hovy
Combining pointer networks~\citep{vinyals2015pointer} with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion.
Ranked #14 on Dependency Parsing on Penn Treebank
1 code implementation • 28 Jul 2021 • PengFei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig
This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning".
1 code implementation • ACL 2021 • PengFei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaicheng Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, Zi-Yi Dou, Graham Neubig
In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e. g.~what is the best-performing system bad at?)
3 code implementations • ACL 2022 • Yixin Liu, PengFei Liu, Dragomir Radev, Graham Neubig
Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary.
Ranked #2 on Text Summarization on X-Sum
3 code implementations • EACL 2021 • Zi-Yi Dou, Graham Neubig
In addition, we demonstrate that we are able to train multilingual word aligners that can obtain robust performance on different language pairs.
1 code implementation • NeurIPS 2021 • Weizhe Yuan, Graham Neubig, PengFei Liu
In this work, we conceptualize the evaluation of generated text as a text generation problem, modeled using pre-trained sequence-to-sequence models.
2 code implementations • 28 Jan 2022 • Uri Alon, Frank F. Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig
Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time.
1 code implementation • 19 Dec 2022 • Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji, Genta Indra Winata, Bryan Wilie, Rahmad Mahendra, Christian Wibisono, Ade Romadhony, Karissa Vincentio, Fajri Koto, JENNIFER SANTOSO, David Moeljadi, Cahya Wirawan, Frederikus Hudi, Ivan Halim Parmonangan, Ika Alfina, Muhammad Satrio Wicaksono, Ilham Firdausi Putra, Samsul Rahmadani, Yulianti Oenang, Ali Akbar Septiandri, James Jaya, Kaustubh D. Dhole, Arie Ardiyanti Suryani, Rifki Afina Putri, Dan Su, Keith Stevens, Made Nindyatama Nityasya, Muhammad Farid Adilazuarda, Ryan Ignatius, Ryandito Diandaru, Tiezheng Yu, Vito Ghifari, Wenliang Dai, Yan Xu, Dyah Damapuspita, Cuk Tho, Ichwanul Muslim Karo Karo, Tirana Noor Fatyanosa, Ziwei Ji, Pascale Fung, Graham Neubig, Timothy Baldwin, Sebastian Ruder, Herry Sujaini, Sakriani Sakti, Ayu Purwarianti
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
2 code implementations • IJCNLP 2019 • Xuezhe Ma, Chunting Zhou, Xi-An Li, Graham Neubig, Eduard Hovy
Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens.
Ranked #3 on Machine Translation on WMT2016 English-Romanian
5 code implementations • ICLR 2020 • Junxian He, Xinyi Wang, Graham Neubig, Taylor Berg-Kirkpatrick
Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach generalizes.
2 code implementations • 13 Jul 2022 • Shuyan Zhou, Uri Alon, Frank F. Xu, Zhiruo Wang, Zhengbao Jiang, Graham Neubig
Publicly available source-code libraries are continuously growing and changing.
2 code implementations • EMNLP 2020 • Jinlan Fu, PengFei Liu, Graham Neubig
With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits.
1 code implementation • EACL 2017 • Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, Graham Neubig, Noah A. Smith
We investigate what information they learn, from a linguistic perspective, through various ablations to the model and the data, and by augmenting the model with an attention mechanism (GA-RNNG) to enable closer inspection.
Ranked #20 on Constituency Parsing on Penn Treebank
1 code implementation • WS 2018 • Graham Neubig, Matthias Sperber, Xinyi Wang, Matthieu Felix, Austin Matthews, Sarguna Padmanabhan, Ye Qi, Devendra Singh Sachan, Philip Arthur, Pierre Godard, John Hewitt, Rachid Riad, Liming Wang
In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing.
2 code implementations • ICLR 2019 • Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick
The variational autoencoder (VAE) is a popular combination of deep latent variable model and accompanying variational learning technique.
Ranked #1 on Text Generation on Yahoo Questions
6 code implementations • ACL 2017 • Pengcheng Yin, Graham Neubig
We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python.
1 code implementation • 30 Jan 2021 • Weizhe Yuan, PengFei Liu, Graham Neubig
The rapid development of science and technology has been accompanied by an exponential growth in peer-reviewed scientific publications.
3 code implementations • NeurIPS 2019 • Paul Michel, Omer Levy, Graham Neubig
Attention is a powerful and ubiquitous mechanism for allowing neural models to focus on particular salient pieces of information by taking their weighted average when making predictions.
1 code implementation • TACL 2020 • Zhengbao Jiang, Frank F. Xu, Jun Araki, Graham Neubig
Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profession".
1 code implementation • 18 Dec 2023 • Syeda Nahida Akter, Zichun Yu, Aashiq Muhamed, Tianyue Ou, Alex Bäuerle, Ángel Alexander Cabrera, Krish Dholakia, Chenyan Xiong, Graham Neubig
The recently released Google Gemini class of models are the first to comprehensively report results that rival the OpenAI GPT series across a wide variety of tasks.
1 code implementation • 14 Nov 2023 • Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, Graham Neubig
To alleviate these problems, we propose FILCO, a method that improves the quality of the context provided to the generator by (1) identifying useful context based on lexical and information-theoretic approaches, and (2) training context filtering models that can filter retrieved contexts at test time.
2 code implementations • 14 Apr 2020 • Keita Kurita, Paul Michel, Graham Neubig
We show that by applying a regularization method, which we call RIPPLe, and an initialization procedure, which we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure.
1 code implementation • 10 Feb 2023 • Shuyan Zhou, Uri Alon, Sumit Agarwal, Graham Neubig
We release five language-specific pretrained models to use with our publicly available code.
2 code implementations • NeurIPS 2017 • Graham Neubig, Yoav Goldberg, Chris Dyer
Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative to toolkits that operate on statically declared computations (e. g., TensorFlow, CNTK, and Theano).
1 code implementation • EMNLP 2020 • Shruti Rijhwani, Antonios Anastasopoulos, Graham Neubig
There is little to no data available to build natural language processing models for most endangered languages.
1 code implementation • 4 Nov 2021 • Shruti Rijhwani, Daisy Rosenblum, Antonios Anastasopoulos, Graham Neubig
In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically-aware decoding method that augments the neural post-correction model with a count-based language model constructed from the recognized texts, implemented using weighted finite-state automata (WFSA) for efficient and effective decoding.
1 code implementation • 24 Jan 2024 • Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents.
1 code implementation • NAACL 2018 • Ye Qi, Devendra Singh Sachan, Matthieu Felix, Sarguna Janani Padmanabhan, Graham Neubig
The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained.
1 code implementation • NAACL 2021 • Zi-Yi Dou, PengFei Liu, Hiroaki Hayashi, Zhengbao Jiang, Graham Neubig
Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control.
2 code implementations • 22 Mar 2021 • David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D'souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Anuoluwapo Aremu, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, Salomey Osei
We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders.
1 code implementation • 22 Oct 2022 • David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba O. Alabi, Shamsuddeen H. Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire M. Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Elvis Mboning, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo L. Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Adeyemi, Gilles Q. Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu Ngoli, Dietrich Klakow
African languages are spoken by over a billion people, but are underrepresented in NLP research and development.
2 code implementations • ACL 2020 • Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, Graham Neubig
Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents.
Ranked #3 on Code Generation on CoNaLa-Ext
3 code implementations • ACL 2020 • Zhengbao Jiang, Wei Xu, Jun Araki, Graham Neubig
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.
Ranked #1 on Relation Extraction on WLPC
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +8
1 code implementation • 23 Feb 2024 • Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, aditi raghunathan
In this work, we address an architectural limitation of autoregressive models: token embeddings cannot contain information from tokens that appear later in the input.
2 code implementations • EMNLP 2017 • Chaitanya Malaviya, Graham Neubig, Patrick Littell
One central mystery of neural NLP is what neural models "know" about their subject matter.
1 code implementation • 13 Oct 2022 • Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, Graham Neubig
In all these natural language tasks, we show that using our approach, a code generation LM (CODEX) outperforms natural-LMs that are fine-tuned on the target task (e. g., T5) and other strong LMs such as GPT-3 in the few-shot setting.
2 code implementations • 15 Feb 2023 • Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob Gardner, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh
Next, we propose a broad range of adaptation strategies for code optimization; for prompting, these include retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play.
1 code implementation • 5 Dec 2022 • Zhengbao Jiang, Luyu Gao, Jun Araki, Haibo Ding, Zhiruo Wang, Jamie Callan, Graham Neubig
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers.
Ranked #1 on Passage Retrieval on Natural Questions
4 code implementations • ACL 2019 • John Wieting, Kevin Gimpel, Graham Neubig, Taylor Berg-Kirkpatrick
We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating paraphrase corpora.
1 code implementation • 30 Apr 2021 • John Wieting, Kevin Gimpel, Graham Neubig, Taylor Berg-Kirkpatrick
We train these models on large amounts of data, achieving significantly improved performance from the original papers proposing the methods on a suite of monolingual semantic similarity, cross-lingual semantic similarity, and bitext mining tasks.
2 code implementations • EMNLP 2021 • Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick
Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints.
1 code implementation • EMNLP 2018 • Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick
In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior.
1 code implementation • ACL 2019 • Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig
Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages.
1 code implementation • EMNLP 2020 • Manik Bhandari, Pranav Gour, Atabak Ashfaq, PengFei Liu, Graham Neubig
Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization.
1 code implementation • EMNLP 2018 • Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime Carbonell
To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order.
2 code implementations • EMNLP 2016 • Philip Arthur, Graham Neubig, Satoshi Nakamura
Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence.
1 code implementation • NAACL 2019 • Paul Michel, Xi-An Li, Graham Neubig, Juan Miguel Pino
Adversarial examples --- perturbations to the input of a model that elicit large changes in the output --- have been shown to be an effective way of assessing the robustness of sequence-to-sequence (seq2seq) models.
1 code implementation • NAACL 2019 • Chunting Zhou, Xuezhe Ma, Di Wang, Graham Neubig
Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages.
2 code implementations • Findings (ACL) 2021 • Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Paco Guzman, Luke Zettlemoyer, Marjan Ghazvininejad
Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input.
2 code implementations • EMNLP 2018 • Paul Michel, Graham Neubig
In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (www. reddit. com) and professionally sourced translations.
1 code implementation • 21 Feb 2022 • Kayo Yin, Graham Neubig
Model interpretability methods are often used to explain NLP model decisions on tasks such as text classification, where the output space is relatively small.
1 code implementation • 7 Jan 2023 • Frank F. Xu, Uri Alon, Graham Neubig
Language models (LMs) compute the probability of a text by sequentially computing a representation of an already-seen context and using this representation to predict the next word.
1 code implementation • 11 Feb 2023 • Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP.
2 code implementations • ICLR 2020 • Zirui Wang, Jiateng Xie, Ruochen Xu, Yiming Yang, Graham Neubig, Jaime Carbonell
Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks.
1 code implementation • NAACL 2021 • Po-Yao Huang, Mandela Patrick, Junjie Hu, Graham Neubig, Florian Metze, Alexander Hauptmann
Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings.
1 code implementation • 14 Sep 2019 • John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel, Graham Neubig
While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation accuracy.
1 code implementation • TACL 2018 • Jacob Buckman, Graham Neubig
In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models.
1 code implementation • 12 Dec 2023 • Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, PengFei Liu
Recent research has made significant strides in applying alignment techniques to enhance the helpfulness and harmlessness of large language models (LLMs) in accordance with human intentions.
1 code implementation • ACL 2021 • Patrick Fernandes, Kayo Yin, Graham Neubig, André F. T. Martins
Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context -- context from sentences other than those currently being translated.
1 code implementation • ACL 2021 • Kayo Yin, Patrick Fernandes, Danish Pruthi, Aditi Chaudhary, André F. T. Martins, Graham Neubig
Are models paying large amounts of attention to the same context?
1 code implementation • IJCNLP 2019 • Bohan Li, Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick, Yiming Yang
In this paper, we investigate a simple fix for posterior collapse which yields surprisingly effective results.
1 code implementation • WS 2018 • Devendra Singh Sachan, Graham Neubig
In multilingual neural machine translation, it has been shown that sharing a single translation model between multiple languages can achieve competitive performance, sometimes even leading to performance gains over bilingually trained models.
1 code implementation • ACL 2018 • Paul Michel, Graham Neubig
Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin.
1 code implementation • EMNLP 2016 • Yuta Kikuchi, Graham Neubig, Ryohei Sasano, Hiroya Takamura, Manabu Okumura
Neural encoder-decoder models have shown great success in many sequence generation tasks.
1 code implementation • 18 Oct 2023 • Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, Maarten Sap
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence.
1 code implementation • 2 Jul 2023 • Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence, Tongshuang Wu, Graham Neubig
In this paper, we ask whether a large language model can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering.
1 code implementation • 2 Oct 2023 • Amanda Bertsch, Alex Xie, Graham Neubig, Matthew R. Gormley
Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine learning system based not on the output with the highest probability, but the output with the lowest risk (expected error) among multiple candidates.
3 code implementations • 29 Jul 2020 • Hao Zhu, Yonatan Bisk, Graham Neubig
In this paper we demonstrate that $\textit{context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies}$.
1 code implementation • ICLR 2021 • Ziyu Yao, Frank F. Xu, Pengcheng Yin, Huan Sun, Graham Neubig
To show the unique benefits of modeling tree edits directly, we further propose a novel edit encoder for learning to represent edits, as well as an imitation learning method that allows the editor to be more robust.
2 code implementations • NAACL 2021 • Mengzhou Xia, Guoqing Zheng, Subhabrata Mukherjee, Milad Shokouhi, Graham Neubig, Ahmed Hassan Awadallah
Extensive experiments on real-world low-resource languages - without access to large-scale monolingual corpora or large amounts of labeled data - for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach.
1 code implementation • EMNLP 2018 • Graham Neubig, Junjie Hu
This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible.
1 code implementation • 5 Dec 2021 • Qibin Chen, Jeremy Lacomis, Edward J. Schwartz, Graham Neubig, Bogdan Vasilescu, Claire Le Goues
Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection.
1 code implementation • EMNLP 2018 • Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, Tom Mitchell
We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation.
1 code implementation • 16 Nov 2020 • Hiroaki Hayashi, Prashant Budania, Peng Wang, Chris Ackerson, Raj Neervannan, Graham Neubig
In this paper, we propose WikiAsp, a large-scale dataset for multi-domain aspect-based summarization that attempts to spur research in the direction of open-domain aspect-based summarization.
1 code implementation • 20 Dec 2022 • Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig
To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset.
1 code implementation • 14 Mar 2024 • Jennifer Hsia, Afreen Shaikh, Zhiruo Wang, Graham Neubig
RAGGED offers further insights into LMs' context utilization habits, where we find that encoder-decoder models rely more on contexts and are thus more sensitive to retrieval quality, while decoder-only models tend to rely on knowledge memorized during training.
5 code implementations • 4 Nov 2016 • Hiroaki Hayashi, Jayanth Koushik, Graham Neubig
Adaptive gradient methods for stochastic optimization adjust the learning rate for each parameter locally.
1 code implementation • EMNLP 2018 • Xinyi Wang, Hieu Pham, Pengcheng Yin, Graham Neubig
Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations.
1 code implementation • EACL 2017 • Jiatao Gu, Graham Neubig, Kyunghyun Cho, Victor O. K. Li
Translating in real-time, a. k. a.
1 code implementation • 11 Nov 2022 • Yau-Shian Wang, Ashley Wu, Graham Neubig
The performance can be further enhanced when cross-lingual NLI data is available.
1 code implementation • ACL 2019 • Zhengbao Jiang, Pengcheng Yin, Graham Neubig
We found that the extraction likelihood, a confidence measure used by current supervised open IE systems, is not well calibrated when comparing the quality of assertions extracted from different sentences.
1 code implementation • 2 Dec 2020 • Zhengbao Jiang, Jun Araki, Haibo Ding, Graham Neubig
We examine this question from the point of view of calibration, the property of a probabilistic model's predicted probabilities actually being well correlated with the probabilities of correctness.
1 code implementation • ACL 2022 • Xinyi Wang, Sebastian Ruder, Graham Neubig
The performance of multilingual pretrained models is highly dependent on the availability of monolingual or parallel text present in a target language.
1 code implementation • ACL 2018 • Harsh Jhamtani, Varun Gangal, Eduard Hovy, Graham Neubig, Taylor Berg-Kirkpatrick
This paper examines the problem of generating natural language descriptions of chess games.
1 code implementation • ICLR 2019 • Xinyi Wang, Hieu Pham, Philip Arthur, Graham Neubig
Multilingual training of neural machine translation (NMT) systems has led to impressive accuracy improvements on low-resource languages.
1 code implementation • ACL 2021 • Vijay Viswanathan, Graham Neubig, PengFei Liu
Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress.
2 code implementations • 23 Mar 2023 • Ivan Stelmakh, John Wieting, Graham Neubig, Nihar B. Shah
We address this challenge by collecting a novel dataset of similarity scores that we release to the research community.
1 code implementation • 30 Jan 2024 • Steffi Chern, Ethan Chern, Graham Neubig, PengFei Liu
Despite the utility of Large Language Models (LLMs) across a wide range of tasks and scenarios, developing a method for reliably evaluating LLMs across varied contexts continues to be challenging.
1 code implementation • NAACL 2016 • Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Chris Dyer
Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation.
1 code implementation • NAACL 2022 • Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, Weizhu Chen
The information in tables can be an important complement to text, making table-based question answering (QA) systems of great value.
Ranked #6 on Semantic Parsing on WikiTableQuestions
1 code implementation • EMNLP 2016 • Graham Neubig, Chris Dyer
Language models (LMs) are statistical models that calculate probabilities over sequences of words or other discrete symbols.
1 code implementation • EMNLP 2018 • Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, Graham Neubig
In models to generate program source code from natural language, representing this code in a tree structure has been a common approach.
2 code implementations • ACL 2019 • Junjie Hu, Mengzhou Xia, Graham Neubig, Jaime Carbonell
It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift.
2 code implementations • ICLR 2019 • Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
We introduce the problem of learning distributed representations of edits.
1 code implementation • 11 Sep 2019 • Junjie Hu, Yu Cheng, Zhe Gan, Jingjing Liu, Jianfeng Gao, Graham Neubig
Previous storytelling approaches mostly focused on optimizing traditional metrics such as BLEU, ROUGE and CIDEr.
Ranked #10 on Visual Storytelling on VIST
1 code implementation • 24 May 2022 • Machel Reid, Graham Neubig
We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.
1 code implementation • 13 Mar 2024 • Ruiyi Wang, Haofei Yu, Wenxin Zhang, Zhengyang Qi, Maarten Sap, Graham Neubig, Yonatan Bisk, Hao Zhu
Motivated by this gap, we propose an interactive learning method, SOTOPIA-$\pi$, improving the social intelligence of language agents.
1 code implementation • ACL 2019 • Junxian He, Zhisong Zhang, Taylor Berg-Kirkpatrick, Graham Neubig
The parameters of source model and target model are softly shared through a regularized log likelihood objective.
1 code implementation • EMNLP 2020 • Zhengbao Jiang, Antonios Anastasopoulos, Jun Araki, Haibo Ding, Graham Neubig
We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages.
1 code implementation • NAACL 2021 • Xinyi Wang, Sebastian Ruder, Graham Neubig
Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary.
1 code implementation • ICLR 2021 • Paul Michel, Tatsunori Hashimoto, Graham Neubig
Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the "uncertainty set").
1 code implementation • ICLR 2022 • Paul Michel, Tatsunori Hashimoto, Graham Neubig
As machine learning models are deployed ever more broadly, it becomes increasingly important that they are not only able to perform well on their training distribution, but also yield accurate predictions when confronted with distribution shift.
1 code implementation • 29 Apr 2022 • Chunting Zhou, Junxian He, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig
One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting.
1 code implementation • NeurIPS 2020 • Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig
While effective, these methods are inefficient at test time as a result of needing to store and index the entire training corpus.
1 code implementation • 16 Mar 2022 • Zhiruo Wang, Grace Cuenca, Shuyan Zhou, Frank F. Xu, Graham Neubig
While there has been a recent burgeoning of applications at the intersection of natural and programming languages, such as code generation and code summarization, these applications are usually English-centric.
1 code implementation • NAACL 2019 • Vaibhav Vaibhav, Sumeet Singh, Craig Stewart, Graham Neubig
Modern Machine Translation (MT) systems perform consistently well on clean, in-domain text.
4 code implementations • IJCNLP 2019 • Antonios Anastasopoulos, Graham Neubig
Recent years have seen exceptional strides in the task of automatic morphological inflection generation.
2 code implementations • ACL 2020 • Xinyi Wang, Yulia Tsvetkov, Graham Neubig
When training multilingual machine translation (MT) models that can translate to/from multiple languages, we are faced with imbalanced training sets: some languages have much more training data than others.
1 code implementation • 1 Nov 2018 • Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga, Graham Neubig, Shoetsu Sato, Masashi Toyoda, Masaru Kitsuregawa
When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities.
1 code implementation • WS 2019 • Shuyan Zhou, Xiangkai Zeng, Yingqi Zhou, Antonios Anastasopoulos, Graham Neubig
While neural machine translation (NMT) achieves remarkable performance on clean, in-domain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise.
1 code implementation • ACL 2020 • Shruti Rijhwani, Shuyan Zhou, Graham Neubig, Jaime Carbonell
However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages.
1 code implementation • 22 Apr 2022 • Patrick Fernandes, Marcos Treviso, Danish Pruthi, André F. T. Martins, Graham Neubig
In this work, leveraging meta-learning techniques, we extend this idea to improve the quality of the explanations themselves, specifically by optimizing explanations such that student models more effectively learn to simulate the original model.
1 code implementation • 26 May 2023 • Vijay Viswanathan, Luyu Gao, Tongshuang Wu, PengFei Liu, Graham Neubig
Using this data, we compare various information retrieval algorithms on our test set and present a superior bi-encoder retriever for text-based dataset recommendation.
1 code implementation • COLING 2018 • Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, Graham Neubig
Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner.
Natural Language Inference Natural Language Understanding +1
2 code implementations • NAACL 2022 • Emmy Liu, Chen Cui, Kenneth Zheng, Graham Neubig
Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition.
1 code implementation • NAACL 2019 • Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, Tom M. Mitchell
In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance.
1 code implementation • IJCNLP 2019 • Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig, Jaime G. Carbonell
Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages.
1 code implementation • EMNLP (nlpbt) 2020 • Frank F. Xu, Lei Ji, Botian Shi, Junyi Du, Graham Neubig, Yonatan Bisk, Nan Duan
Watching instructional videos are often used to learn about procedures.
3 code implementations • ACL 2020 • Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton
Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing.
1 code implementation • TACL 2020 • Shuyan Zhou, Shruti Rijhawani, John Wieting, Jaime Carbonell, Graham Neubig
Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts.
1 code implementation • ACL 2020 • Mengzhou Xia, Antonios Anastasopoulos, Ruochen Xu, Yiming Yang, Graham Neubig
Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Danish Pruthi, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton
For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness.
1 code implementation • EMNLP 2021 • Chunting Zhou, Daniel Levy, Xian Li, Marjan Ghazvininejad, Graham Neubig
Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of deployed models.
1 code implementation • NAACL (SUKI) 2022 • Zhiruo Wang, Zhengbao Jiang, Eric Nyberg, Graham Neubig
In this work, we focus on the task of table retrieval, and ask: "is table-specific model design necessary for table retrieval, or can a simpler text-based model be effectively used to achieve a similar result?"
2 code implementations • ACL 2017 • Frederick Liu, Han Lu, Chieh Lo, Graham Neubig
Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words.
1 code implementation • ACL 2019 • Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg, Matthew R. Gormley, Graham Neubig
We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.
1 code implementation • 14 Jun 2021 • Chunting Zhou, Xuezhe Ma, Paul Michel, Graham Neubig
Group distributionally robust optimization (DRO) provides an effective tool to alleviate covariate shift by minimizing the worst-case training loss over a set of pre-defined groups.
1 code implementation • NAACL 2022 • Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, André F. T. Martins
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search.
1 code implementation • 23 Jan 2024 • Zhiruo Wang, Daniel Fried, Graham Neubig
Language models (LMs) can solve tasks such as answering questions about tables or images by writing programs.
1 code implementation • ACL 2020 • Antonios Anastasopoulos, Graham Neubig
Most of recent work in cross-lingual word embeddings is severely Anglocentric.
1 code implementation • Findings (EMNLP) 2021 • Xinyi Wang, Yulia Tsvetkov, Sebastian Ruder, Graham Neubig
Adapters are light-weight modules that allow parameter-efficient fine-tuning of pretrained models.
1 code implementation • ACL 2022 • Shuyan Zhou, Li Zhang, Yue Yang, Qing Lyu, Pengcheng Yin, Chris Callison-Burch, Graham Neubig
To this end, we develop a simple and efficient method that links steps (e. g., "purchase a camera") in an article to other articles with similar goals (e. g., "how to choose a camera"), recursively constructing the KB.
1 code implementation • 2 Mar 2023 • Andy Liu, Hao Zhu, Emmy Liu, Yonatan Bisk, Graham Neubig
We also find some evidence that increasing task difficulty in the training process results in more fluent and precise utterances in evaluation.
1 code implementation • 8 Feb 2024 • Lucio Dery, Steven Kolawole, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar
Given the generational gap in available hardware between lay practitioners and the most endowed institutions, LLMs are becoming increasingly inaccessible as they grow in size.
1 code implementation • 9 Nov 2018 • Shruti Rijhwani, Jiateng Xie, Graham Neubig, Jaime Carbonell
To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language.
1 code implementation • 29 Nov 2019 • Ansong Ni, Pengcheng Yin, Graham Neubig
Experiments on WikiTableQuestions with human annotators show that our method can improve the performance with only 100 active queries, especially for weakly-supervised parsers learnt from a cold start.
1 code implementation • EMNLP 2017 • Varun Gangal, Harsh Jhamtani, Graham Neubig, Eduard Hovy, Eric Nyberg
Portmanteaus are a word formation phenomenon where two words are combined to form a new word.
2 code implementations • EMNLP 2020 • John Wieting, Graham Neubig, Taylor Berg-Kirkpatrick
Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences.
1 code implementation • NAACL 2021 • Yixin Liu, Graham Neubig, John Wieting
In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task.
1 code implementation • EMNLP 2021 • Machel Reid, Junjie Hu, Graham Neubig, Yutaka Matsuo
Reproducible benchmarks are crucial in driving progress of machine translation research.
1 code implementation • 1 Jul 2022 • Perez Ogayo, Graham Neubig, Alan W Black
This paper focuses on speech synthesis for low-resourced African languages, from corpus creation to sharing and deploying the Text-to-Speech (TTS) systems.
1 code implementation • EMNLP 2018 • Aditi Chaudhary, Chunting Zhou, Lori Levin, Graham Neubig, David R. Mortensen, Jaime G. Carbonell
Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging.
1 code implementation • IJCNLP 2019 • Zi-Yi Dou, Junjie Hu, Antonios Anastasopoulos, Graham Neubig
The recent success of neural machine translation models relies on the availability of high quality, in-domain data.
1 code implementation • EMNLP 2021 • Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov
Text generation systems are ubiquitous in natural language processing applications.
2 code implementations • ICLR 2022 • Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig
In most settings of practical concern, machine learning practitioners know in advance what end-task they wish to boost with auxiliary tasks.
1 code implementation • 7 Nov 2023 • Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu, Ameet Talwalkar, Graham Neubig
As large language models (LLMs) become more capable, there is growing excitement about the possibility of using LLMs as proxies for humans in real-world tasks where subjective labels are desired, such as in surveys and opinion polling.
1 code implementation • NAACL 2019 • Nikolai Vogler, Craig Stewart, Graham Neubig
Simultaneous interpretation, the translation of speech from one language to another in real-time, is an inherently difficult and strenuous task.
1 code implementation • EMNLP 2020 • Zi-Yi Dou, Antonios Anastasopoulos, Graham Neubig
Back-translation has proven to be an effective method to utilize monolingual data in neural machine translation (NMT), and iteratively conducting back-translation can further improve the model performance.
1 code implementation • ACL 2022 • Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Meza-Ruiz, Gustavo A. Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Ngoc Thang Vu, Katharina Kann
Continued pretraining offers improvements, with an average accuracy of 44. 05%.
2 code implementations • 13 Oct 2021 • Damián Blasi, Antonios Anastasopoulos, Graham Neubig
Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development.
1 code implementation • AKBC 2020 • Zhengbao Jiang, Jun Araki, Donghan Yu, Ruohong Zhang, Wei Xu, Yiming Yang, Graham Neubig
We propose several methods that incorporate both structured and textual information to represent relations for this task.
1 code implementation • ACL 2022 • Damian Blasi, Antonios Anastasopoulos, Graham Neubig
Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development.
1 code implementation • 27 Oct 2022 • Amanda Bertsch, Graham Neubig, Matthew R. Gormley
As a sample application, we demonstrate that applying perspective shifting to a dialogue summarization dataset (SAMSum) substantially improves the zero-shot performance of extractive news summarization models on this data.
1 code implementation • ACL 2018 • Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, Graham Neubig
Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding.
1 code implementation • NAACL 2018 • Yohan Jo, Shivani Poddar, Byungsoo Jeon, Qinlan Shen, Carolyn P. Rose, Graham Neubig
We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument, with the goal of predicting if the argument successfully changes the OH's view.
1 code implementation • IJCNLP 2019 • Chunting Zhou, Xuezhe Ma, Junjie Hu, Graham Neubig
Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs.
1 code implementation • EACL 2021 • Zihuiwen Ye, PengFei Liu, Jinlan Fu, Graham Neubig
We perform an analysis of four types of NLP tasks, and both demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future.
1 code implementation • 26 Mar 2018 • Matthias Sperber, Jan Niehues, Graham Neubig, Sebastian Stüker, Alex Waibel
Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities.
1 code implementation • WS 2019 • Shuyan Zhou, Shruti Rijhwani, Graham Neubig
Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia.
1 code implementation • ICML 2020 • Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, Graham Neubig
To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content or practice problems.
1 code implementation • 24 Apr 2020 • Aman Madaan, Shruti Rijhwani, Antonios Anastasopoulos, Yiming Yang, Graham Neubig
We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators.
1 code implementation • 7 Oct 2022 • Emmy Liu, Graham Neubig
We find that the representation of a parent phrase can be predicted with some accuracy given an affine transformation of its children.
1 code implementation • 1 Jun 2023 • Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra, Patrick Fernandes, PengFei Liu, Graham Neubig, Chunting Zhou
Most frameworks that perform such multi-dimensional evaluation require training on large manually or synthetically generated datasets.
1 code implementation • 14 Sep 2023 • Nathaniel R. Robinson, Perez Ogayo, David R. Mortensen, Graham Neubig
Without published experimental evidence on the matter, it is difficult for speakers of the world's diverse languages to know how and whether they can use LLMs for their languages.
1 code implementation • 3 Apr 2020 • Samuel Läubli, Sheila Castilho, Graham Neubig, Rico Sennrich, Qinlan Shen, Antonio Toral
The quality of machine translation has increased remarkably over the past years, to the degree that it was found to be indistinguishable from professional human translation in a number of empirical investigations.
1 code implementation • 1 Dec 2020 • Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, William W. Cohen
While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated.
1 code implementation • 13 Sep 2021 • Aditi Chaudhary, Kayo Yin, Antonios Anastasopoulos, Graham Neubig
Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language.
1 code implementation • CoNLL (EMNLP) 2021 • Ruisi Su, Shruti Rijhwani, Hao Zhu, Junxian He, Xinyu Wang, Yonatan Bisk, Graham Neubig
Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50\% as compared to state-of-the-art models trained on pure text.
1 code implementation • 5 Dec 2023 • Atharva Kulkarni, Lucio Dery, Amrith Setlur, aditi raghunathan, Ameet Talwalkar, Graham Neubig
We primarily consider the standard setting of fine-tuning a pre-trained model, where, following recent work \citep{gururangan2020don, dery2023aang}, we multitask the end task with the pre-training objective constructed from the end task data itself.
1 code implementation • WS 2018 • Junjie Hu, Wei-Cheng Chang, Yuexin Wu, Graham Neubig
In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach.
1 code implementation • EMNLP 2020 • Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig
Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data.
1 code implementation • 12 Dec 2022 • Yiwei Qin, Weizhe Yuan, Graham Neubig, PengFei Liu
Both have their advantages; discriminative metrics are able to directly optimize for the problem of distinguishing between good and bad outputs, while generative metrics can be trained using abundant raw text.