Ellie Pavlick
9 papers in the PaperMetrix corpus
Papers by this author
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What Happens To BERT Embeddings During Fine-tuning?
2020
While much recent work has examined how linguistic information is encoded in pretrained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks.
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Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling\n Denotation and Connotation in Vector Spaces
2020 · arXiv (Cornell University)
In politics, neologisms are frequently invented for partisan objectives. For\nexample, "undocumented workers" and "illegal aliens" refer to the same group of\npeople (i.e., they have the same denotation), but they carry clearly different\nconnotations. Examples like these …
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Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
2022 · arXiv (Cornell University)
Large-scale neural network models combining text and images have made incredible progress in recent years. However, it remains an open question to what extent such models encode compositional representations of the concepts over which they …
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Optimizing Statistical Machine Translation for Text Simplification
2016 · Transactions of the Association for Computational Linguistics
Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, …
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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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BERT Rediscovers the Classical NLP Pipeline
2019
Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We …
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Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
2019
A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within …
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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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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 …