Search Results for author: Patrick Xia

Found 19 papers, 6 papers with code

Pruning Pretrained Encoders with a Multitask Objective

no code implementations10 Dec 2021 Patrick Xia, Richard Shin

The sizes of pretrained language models make them challenging and expensive to use when there are multiple desired downstream tasks.

Pretrained Language Models

On Generalization in Coreference Resolution

1 code implementation CRAC (ACL) 2021 Shubham Toshniwal, Patrick Xia, Sam Wiseman, Karen Livescu, Kevin Gimpel

While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains.

Coreference Resolution Data Augmentation

Moving on from OntoNotes: Coreference Resolution Model Transfer

1 code implementation EMNLP 2021 Patrick Xia, Benjamin Van Durme

Academic neural models for coreference resolution (coref) are typically trained on a single dataset, OntoNotes, and model improvements are benchmarked on that same dataset.

Coreference Resolution

Incremental Neural Coreference Resolution in Constant Memory

no code implementations EMNLP 2020 Patrick Xia, João Sedoc, Benjamin Van Durme

We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components.

Coreference Resolution

Multi-Sentence Argument Linking

no code implementations ACL 2020 Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, Benjamin Van Durme

We present a novel document-level model for finding argument spans that fill an event's roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution.

Coreference Resolution Semantic Role Labeling +1

Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting

1 code implementation NAACL 2019 J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme

Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting.

Data Augmentation Machine Translation +3

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 +1

CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages

no code implementations CONLL 2017 Ryan Cotterell, Christo Kirov, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia, Manaal Faruqui, Sandra Kübler, David Yarowsky, Jason Eisner, Mans Hulden

In sub-task 2, systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms.

Data Augmentation

Annotating Character Relationships in Literary Texts

no code implementations2 Dec 2015 Philip Massey, Patrick Xia, David Bamman, Noah A. Smith

We present a dataset of manually annotated relationships between characters in literary texts, in order to support the training and evaluation of automatic methods for relation type prediction in this domain (Makazhanov et al., 2014; Kokkinakis, 2013) and the broader computational analysis of literary character (Elson et al., 2010; Bamman et al., 2014; Vala et al., 2015; Flekova and Gurevych, 2015).

Type prediction

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