Alex Wang
8 papers in the PaperMetrix corpus
Papers by this author
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BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
2019 · arXiv (Cornell University)
We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that …
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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
2018 · arXiv (Cornell University)
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 …
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Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
2019
Najoung Kim, Roma Patel, Adam Poliak, Patrick Xia, Alex Wang, Tom McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, Ellie Pavlick. Proceedings of the Eighth Joint Conference on Lexical and …
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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
2019 · arXiv (Cornell University)
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. The GLUE benchmark, introduced a little over one year ago, …
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A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models
2019 · arXiv (Cornell University)
Undirected neural sequence models such as BERT (Devlin et al., 2019) have received renewed interest due to their success on discriminative natural language understanding tasks such as question-answering and natural language inference. The problem of …
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Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
2019
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, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman. …
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Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
2020
Practical applications of abstractive summarization models are limited by frequent factual inconsistencies with respect to their input. Existing automatic evaluation metrics for summarization are largely insensitive to such errors. We propose QAGS, 1 an automatic …
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What do you learn from context? Probing for sentence structure in\n contextualized word representations
2019 · arXiv (Cornell University)
Contextualized representation models such as ELMo (Peters et al., 2018a) and\nBERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a\ndiverse array of downstream NLP tasks. Building on recent token-level probing\nwork, we introduce a …