Search Results for author: Alex Wang

Found 27 papers, 12 papers with code

Overview of the SustaiNLP 2020 Shared Task

no code implementations EMNLP (sustainlp) 2020 Alex Wang, Thomas Wolf

We describe the SustaiNLP 2020 shared task: efficient inference on the SuperGLUE benchmark (Wang et al., 2019).

JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation

no code implementations29 Oct 2023 Yao Yao, Peike Li, BoYu Chen, Alex Wang

With rapid advances in generative artificial intelligence, the text-to-music synthesis task has emerged as a promising direction for music generation from scratch.

Music Generation

When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

no code implementations8 Sep 2023 Max Marion, Ahmet Üstün, Luiza Pozzobon, Alex Wang, Marzieh Fadaee, Sara Hooker

In this work, we take a wider view and explore scalable estimates of data quality that can be used to systematically measure the quality of pretraining data.


JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models

1 code implementation9 Aug 2023 Peike Li, BoYu Chen, Yao Yao, Yikai Wang, Allen Wang, Alex Wang

Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization.

Music Generation Text-to-Music Generation

GLARE: A Dataset for Traffic Sign Detection in Sun Glare

1 code implementation19 Sep 2022 Nicholas Gray, Megan Moraes, Jiang Bian, Allen Tian, Alex Wang, Haoyi Xiong, Zhishan Guo

Real-time machine learning detection algorithms are often found within autonomous vehicle technology and depend on quality datasets.

Traffic Sign Detection Traffic Sign Recognition

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

SQuALITY: Building a Long-Document Summarization Dataset the Hard Way

1 code implementation23 May 2022 Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, Samuel R. Bowman

Summarization datasets are often assembled either by scraping naturally occurring public-domain summaries -- which are nearly always in difficult-to-work-with technical domains -- or by using approximate heuristics to extract them from everyday text -- which frequently yields unfaithful summaries.

Document Summarization Multiple-choice

QuestEval: Summarization Asks for Fact-based Evaluation

2 code implementations EMNLP 2021 Thomas Scialom, Paul-Alexis Dray, Patrick Gallinari, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang

Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments.

Question Answering

Label Representations in Modeling Classification as Text Generation

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Xinyi Chen, Jingxian Xu, Alex Wang

Several recent state-of-the-art transfer learning methods model classification tasks as text generation, where labels are represented as strings for the model to generate.

text-classification Text Classification +2

Asking and Answering Questions to Evaluate the Factual Consistency of Summaries

2 code implementations ACL 2020 Alex Wang, Kyunghyun Cho, Mike Lewis

QAGS is based on the intuition that if we ask questions about a summary and its source, we will receive similar answers if the summary is factually consistent with the source.

Abstractive Text Summarization

A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models

1 code implementation29 May 2019 Elman Mansimov, Alex Wang, Sean Welleck, Kyunghyun Cho

We investigate this problem by proposing a generalized model of sequence generation that unifies decoding in directed and undirected models.

Machine Translation Natural Language Inference +3

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

5 code implementations NeurIPS 2019 Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman

In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks.

Transfer Learning

Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling

no code implementations ICLR 2019 Samuel R. Bowman, Ellie Pavlick, Edouard Grave, Benjamin Van Durme, Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen

Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018).

Language Modelling

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension

no code implementations SEMEVAL 2019 Najoung Kim, Roma Patel, Adam Poliak, Alex Wang, Patrick Xia, R. Thomas McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, Ellie Pavlick

Our results show that pretraining on language modeling performs the best on average across our probing tasks, supporting its widespread use for pretraining state-of-the-art NLP models, and CCG supertagging and NLI pretraining perform comparably.

CCG Supertagging Language Modelling +2

On Measuring Social Biases in Sentence Encoders

1 code implementation NAACL 2019 Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, Rachel Rudinger

The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017).

Test Word Embeddings

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

11 code implementations WS 2018 Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman

For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset.

Natural Language Inference Natural Language Understanding +2

Clustering Stable Instances of Euclidean k-means

no code implementations4 Dec 2017 Abhratanu Dutta, Aravindan Vijayaraghavan, Alex Wang

We design efficient algorithms that provably recover the optimal clustering for instances that are additive perturbation stable.


Clustering Stable Instances of Euclidean k-means.

no code implementations NeurIPS 2017 Aravindan Vijayaraghavan, Abhratanu Dutta, Alex Wang

To address this disconnect, we study the following question: what properties of real-world instances will enable us to design efficient algorithms and prove guarantees for finding the optimal clustering?


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