no code implementations • EMNLP 2020 • Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, Clare Voss
Event schemas can guide our understanding and ability to make predictions with respect to what might happen next.
no code implementations • NAACL (ACL) 2022 • Ashutosh Joshi, Shankar Vishwanath, Choon Teo, Vaclav Petricek, Vishy Vishwanathan, Rahul Bhagat, Jonathan May
We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation.
1 code implementation • NAACL 2022 • Alexander Spangher, Xiang Ren, Jonathan May, Nanyun Peng
News article revision histories provide clues to narrative and factual evolution in news articles.
no code implementations • EMNLP 2021 • Alexander Spangher, Jonathan May, Sz-Rung Shiang, Lingjia Deng
As labeling schemas evolve over time, small differences can render datasets following older schemas unusable.
Ranked #1 on Text Classification on NewsDiscourse
1 code implementation • 12 Apr 2024 • Xuezhe Ma, Xiaomeng Yang, Wenhan Xiong, Beidi Chen, Lili Yu, Hao Zhang, Jonathan May, Luke Zettlemoyer, Omer Levy, Chunting Zhou
The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy.
1 code implementation • 12 Mar 2024 • Shuai Liu, Shantanu Agarwal, Jonathan May
Authorship style transfer aims to rewrite a given text into a specified target while preserving the original meaning in the source.
no code implementations • 16 Nov 2023 • Alexander Spangher, Emilio Ferrara, Ben Welsh, Nanyun Peng, Serdar Tumgoren, Jonathan May
Journalists must find stories in huge amounts of textual data (e. g. leaks, bills, press releases) as part of their jobs: determining when and why text becomes news can help us understand coverage patterns and help us build assistive tools.
no code implementations • 16 Nov 2023 • Hyundong Cho, Shuai Liu, Taiwei Shi, Darpan Jain, Basem Rizk, YuYang Huang, Zixun Lu, Nuan Wen, Jonathan Gratch, Emilio Ferrera, Jonathan May
Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators.
no code implementations • 15 Sep 2023 • Meryem M'hamdi, Jonathan May, Franck Dernoncourt, Trung Bui, Seunghyun Yoon
Our approach leverages meta-distillation learning based on MAML, an optimization-based Model-Agnostic Meta-Learner.
1 code implementation • 26 Jun 2023 • Virginia K. Felkner, Ho-Chun Herbert Chang, Eugene Jang, Jonathan May
We present WinoQueer: a benchmark specifically designed to measure whether large language models (LLMs) encode biases that are harmful to the LGBTQ+ community.
1 code implementation • 12 Jun 2023 • Shuai Liu, Hyundong J. Cho, Marjorie Freedman, Xuezhe Ma, Jonathan May
Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge.
1 code implementation • 24 May 2023 • Alexander Spangher, Nanyun Peng, Jonathan May, Emilio Ferrara
News articles are driven by the informational sources journalists use in reporting.
1 code implementation • 23 May 2023 • Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Raghavi Chandu, Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services.
1 code implementation • 23 May 2023 • Linghao Jin, Jacqueline He, Jonathan May, Xuezhe Ma
Context-aware neural machine translation involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, and has given rise to a number of recent techniques.
no code implementations • 18 May 2023 • Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Young Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park
Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter.
1 code implementation • 16 May 2023 • Zihao He, Jonathan May, Kristina Lerman
Detecting norm violations in online communities is critical to maintaining healthy and safe spaces for online discussions.
no code implementations • 1 Dec 2022 • Kai Chen, Zihao He, Rong-Ching Chang, Jonathan May, Kristina Lerman
We collect discussions from a wide variety of topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc.
no code implementations • 11 Oct 2022 • Thamme Gowda, Mozhdeh Gheini, Jonathan May
Code-switching is a common phenomenon among multilingual speakers, where alternation between two or more languages occurs within the context of a single conversation.
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
no code implementations • 23 Jun 2022 • Virginia K. Felkner, Ho-Chun Herbert Chang, Eugene Jang, Jonathan May
This paper presents exploratory work on whether and to what extent biases against queer and trans people are encoded in large language models (LLMs) such as BERT.
1 code implementation • 14 Jun 2022 • Alexander Spangher, Xiang Ren, Jonathan May, Nanyun Peng
News article revision histories provide clues to narrative and factual evolution in news articles.
no code implementations • 25 May 2022 • Jiao Sun, Swabha Swayamdipta, Jonathan May, Xuezhe Ma
After controlling for instances where rationales leak the correct answer while not providing additional background knowledge, we find that incorporating only 5% of rationales during training can boost model performance by 47. 22% for CoS-E and 57. 14% for ECQA during inference.
no code implementations • 25 May 2022 • Nada Aldarrab, Jonathan May
In this work, we propose the first automatic methods to segment those ciphers using Byte Pair Encoding (BPE) and unigram language models.
no code implementations • 25 May 2022 • Mozhdeh Gheini, Xuezhe Ma, Jonathan May
A recent family of techniques, dubbed lightweight fine-tuning methods, facilitates parameter-efficient transfer learning by updating only a small set of additional parameters while keeping the parameters of the pretrained language model frozen.
1 code implementation • 25 May 2022 • Jacob Bremerman, Xiang Ren, Jonathan May
We find that existing MT models fail when presented with NAV data, but we demonstrate strategies to improve performance on NAV by fine-tuning them with human-generated variations.
1 code implementation • 23 May 2022 • Meryem M'hamdi, Xiang Ren, Jonathan May
The longstanding goal of multi-lingual learning has been to develop a universal cross-lingual model that can withstand the changes in multi-lingual data distributions.
1 code implementation • Findings (NAACL) 2022 • Kushal Chawla, Gale M. Lucas, Jonathan May, Jonathan Gratch
A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training.
2 code implementations • 15 Dec 2021 • Hyundong Cho, Chinnadhurai Sankar, Christopher Lin, Kaushik Ram Sadagopan, Shahin Shayandeh, Asli Celikyilmaz, Jonathan May, Ahmad Beirami
Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance.
Ranked #4 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)
Dialogue State Tracking Multi-domain Dialogue State Tracking +1
no code implementations • 8 Nov 2021 • Hengameh Mirzaalian, Mohamed E. Hussein, Leonidas Spinoulas, Jonathan May, Wael Abd-Almageed
Due to the limited amount of annotated data in our study, we apply a light-weight LSTM network as our natural language generation model.
no code implementations • EMNLP 2021 • Xiyang Zhang, Muhao Chen, Jonathan May
Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process.
no code implementations • 25 Aug 2021 • Hyundong Cho, Basel Shbita, Kartik Shenoy, Shuai Liu, Nikhil Patel, Hitesh Pindikanti, Jennifer Lee, Jonathan May
We present Viola, an open-domain dialogue system for spoken conversation that uses a topic-agnostic dialogue manager based on a simple generate-and-rank approach.
2 code implementations • NeurIPS 2021 • Xuezhe Ma, Xiang Kong, Sinong Wang, Chunting Zhou, Jonathan May, Hao Ma, Luke Zettlemoyer
Specifically, with the first attention function, Luna packs the input sequence into a sequence of fixed length.
no code implementations • 20 Apr 2021 • Alexander Spangher, Jonathan May
In this work, we create a web application to highlight the output of NLP models trained to parse and label discourse segments in law text.
1 code implementation • NAACL 2021 • Meryem M'hamdi, Doo Soon Kim, Franck Dernoncourt, Trung Bui, Xiang Ren, Jonathan May
We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering.
no code implementations • 19 Apr 2021 • Alexander Spangher, Nanyun Peng, Jonathan May, Emilio Ferrara
Journalists obtain "leads", or story ideas, by reading large corpora of government records: court cases, proposed bills, etc.
1 code implementation • 19 Apr 2021 • Alexander Spangher, Nanyun Peng, Jonathan May, Emilio Ferrara
Journalists publish statements provided by people, or \textit{sources} to contextualize current events, help voters make informed decisions, and hold powerful individuals accountable.
no code implementations • 19 Apr 2021 • Alexander Spangher, Jonathan May
In this work, we present, to our knowledge, the first publicly available dataset of news article revision histories, or NewsEdits.
1 code implementation • EMNLP 2021 • Mozhdeh Gheini, Xiang Ren, Jonathan May
We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross-attention when training from scratch.
1 code implementation • NAACL 2021 • Thamme Gowda, Weiqiu You, Constantine Lignos, Jonathan May
While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy.
2 code implementations • ACL 2021 • Thamme Gowda, Zhao Zhang, Chris A Mattmann, Jonathan May
While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages.
1 code implementation • NAACL 2021 • Kushal Chawla, Jaysa Ramirez, Rene Clever, Gale Lucas, Jonathan May, Jonathan Gratch
Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI.
no code implementations • 2 Jan 2021 • Alexander Spangher, Jonathan May, Sz-Rung Shiang, Lingjia Deng
Small class-imbalanced datasets, common in many high-level semantic tasks like discourse analysis, present a particular challenge to current deep-learning architectures.
1 code implementation • ACL 2021 • Karen Hambardzumyan, Hrant Khachatrian, Jonathan May
Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks.
no code implementations • ACL 2021 • Nada Aldarrab, Jonathan May
Decipherment of historical ciphers is a challenging problem.
no code implementations • ACL (dialdoc) 2021 • Xusen Yin, Li Zhou, Kevin Small, Jonathan May
Our model shows SOTA performance of SQ generation on the NQ dataset (20. 1 BLEU-4).
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Xusen Yin, Ralph Weischedel, Jonathan May
However, the large amount of computation necessary to adequately train and explore the search space of sequential decision making, under a reinforcement learning paradigm, precludes the inclusion of large contextualized language models, which might otherwise enable the desired generalization ability.
no code implementations • LREC 2020 • Di Lu, Ananya Subburathinam, Heng Ji, Jonathan May, Shih-Fu Chang, Avi Sil, Clare Voss
Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging.
2 code implementations • EMNLP 2020 • Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian
Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates.
1 code implementation • ACL 2020 • Hyundong Cho, Jonathan May
Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people.
no code implementations • 6 Apr 2020 • Xusen Yin, Jonathan May
Reinforcement learning algorithms such as Q-learning have shown great promise in training models to learn the optimal action to take for a given system state; a goal in applications with an exploratory or adversarial nature such as task-oriented dialogues or games.
no code implementations • 6 Apr 2020 • Kushal Chawla, Gale Lucas, Jonathan May, Jonathan Gratch
Agents that negotiate with humans find broad applications in pedagogy and conversational AI.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Thamme Gowda, Jonathan May
We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components.
no code implementations • IJCNLP 2019 • Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss
The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages.
no code implementations • IJCNLP 2019 • James Mullenbach, Jonathan Gordon, Nanyun Peng, Jonathan May
This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings.
no code implementations • CONLL 2019 • Meryem M{'}hamdi, Marjorie Freedman, Jonathan May
Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.
no code implementations • WS 2019 • Xiaoman Pan, Thamme Gowda, Heng Ji, Jonathan May, Scott Miller
Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking.
no code implementations • 14 Sep 2019 • Mozhdeh Gheini, Jonathan May
In this work, we present a `universal' pre-trained neural parent model with constant vocabulary that can be used as a starting point for training practically any new low-resource language to a fixed target language.
no code implementations • IJCNLP 2019 • Xiaolei Huang, Jonathan May, Nanyun Peng
While recent work has shown promising results on cross-lingual transfer from high-resource languages to low-resource languages, it is unclear what knowledge is transferred.
1 code implementation • 13 Aug 2019 • Xusen Yin, Jonathan May
We consider the task of learning to play families of text-based computer adventure games, i. e., fully textual environments with a common theme (e. g. cooking) and goal (e. g. prepare a meal from a recipe) but with different specifics; new instances of such games are relatively straightforward for humans to master after a brief exposure to the genre but have been curiously difficult for computer agents to learn.
no code implementations • ACL 2019 • Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed.
no code implementations • ACL 2019 • Nima Pourdamghani, Nada Aldarrab, Marjan Ghazvininejad, Kevin Knight, Jonathan May
Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation.
no code implementations • NAACL 2019 • Lifu Huang, Heng Ji, Jonathan May
We focus on improving name tagging for low-resource languages using annotations from related languages.
2 code implementations • 6 May 2019 • Xusen Yin, Jonathan May
As such, an LSTM-based DQN can take tens of days to finish the training process.
1 code implementation • NAACL 2019 • Ronald Cardenas, Ying Lin, Heng Ji, Jonathan May
We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available.
no code implementations • 16 Aug 2018 • Nelson F. Liu, Jonathan May, Michael Pust, Kevin Knight
Most statistical machine translation systems cannot translate words that are unseen in the training data.
no code implementations • ACL 2018 • Ulf Hermjakob, Jonathan May, Michael Pust, Kevin Knight
In a corruption of John Searle{'}s famous AI thought experiment, the Chinese Room (Searle, 1980), we twist its original intent by enabling humans to translate text, e. g. from Uyghur to English, even if they don{'}t have any prior knowledge of the source language.
no code implementations • ACL 2018 • Ulf Hermjakob, Jonathan May, Kevin Knight
We present uroman, a tool for converting text in myriads of languages and scripts such as Chinese, Arabic and Cyrillic into a common Latin-script representation.
no code implementations • WS 2018 • Nanyun Peng, Marjan Ghazvininejad, Jonathan May, Kevin Knight
We present a general framework of analyzing existing story corpora to generate controllable and creative new stories.
no code implementations • NAACL 2018 • Boliang Zhang, Ying Lin, Xiaoman Pan, Di Lu, Jonathan May, Kevin Knight, Heng Ji
We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap.
no code implementations • NAACL 2018 • Yining Chen, Sorcha Gilroy, Andreas Maletti, Jonathan May, Kevin Knight
We investigate the computational complexity of various problems for simple recurrent neural networks (RNNs) as formal models for recognizing weighted languages.
no code implementations • SEMEVAL 2017 • Jonathan May, Jay Priyadarshi
In the generation subtask, participants were asked to generate English sentences given AMR graphs in the news/forum domain.
no code implementations • ACL 2017 • Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, Heng Ji
The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia.
no code implementations • LREC 2016 • Eunsol Choi, Matic Horvat, Jonathan May, Kevin Knight, Daniel Marcu
Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work.
1 code implementation • EMNLP 2016 • Barret Zoph, Deniz Yuret, Jonathan May, Kevin Knight
Ensembling and unknown word replacement add another 2 Bleu which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair.
no code implementations • 10 Mar 2016 • Lifu Huang, Jonathan May, Xiaoman Pan, Heng Ji
Recent research has shown great progress on fine-grained entity typing.
no code implementations • 24 Apr 2015 • Michael Pust, Ulf Hermjakob, Kevin Knight, Daniel Marcu, Jonathan May
To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling.
no code implementations • LREC 2012 • Daniele Pighin, Llu{\'\i}s M{\`a}rquez, Jonathan May
We present an annotated resource consisting of open-domain translation requests, automatic translations and user-provided corrections collected from casual users of the translation portal http://reverso. net.