no code implementations • COLING (CRAC) 2022 • Patrick Xia, Benjamin Van Durme
Humans process natural language online, whether reading a document or participating in multiparty dialogue.
1 code implementation • 16 Nov 2023 • Nikita Moghe, Patrick Xia, Jacob Andreas, Jason Eisner, Benjamin Van Durme, Harsh Jhamtani
Users of natural language interfaces, generally powered by Large Language Models (LLMs), often must repeat their preferences each time they make a similar request.
1 code implementation • 20 Sep 2023 • Kumar Shridhar, Harsh Jhamtani, Hao Fang, Benjamin Van Durme, Jason Eisner, Patrick Xia
To enable exploration in this space, we present SCREWS, a modular framework for reasoning with revisions.
1 code implementation • 18 Sep 2023 • Kevin Lin, Patrick Xia, Hao Fang
We evaluate the ability of semantic parsers based on large language models (LLMs) to handle contextual utterances.
no code implementations • 15 May 2023 • Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, Ben Van Durme
Designing natural language interfaces has historically required collecting supervised data to translate user requests into carefully designed intent representations.
no code implementations • 20 Oct 2022 • Yukun Feng, Patrick Xia, Benjamin Van Durme, João Sedoc
Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance?
1 code implementation • 2 Aug 2022 • Boyuan Zheng, Patrick Xia, Mahsa Yarmohammadi, Benjamin Van Durme
Existing multiparty dialogue datasets for entity coreference resolution are nascent, and many challenges are still unaddressed.
no code implementations • 10 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.
2 code implementations • 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.
Ranked #1 on Coreference Resolution on WikiCoref
2 code implementations • 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.
1 code implementation • ACL 2022 • Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, Jordan Boyd-Graber
Active learning mitigates this problem by sampling a small subset of data for annotators to label.
no code implementations • EACL 2021 • Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme
We present LOME, a system for performing multilingual information extraction.
no code implementations • EMNLP (spnlp) 2020 • Abhinav Singh, Patrick Xia, Guanghui Qin, Mahsa Yarmohammadi, Benjamin Van Durme
Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output.
no code implementations • EMNLP 2020 • Patrick Xia, Shijie Wu, Benjamin Van Durme
Pretrained contextualized text encoders are now a staple of the NLP community.
no code implementations • LREC 2020 • Arya D. McCarthy, Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia, Kyle Gorman, Ekaterina Vylomova, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, Timofey Arkhangelskiy, Nataly Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ern{\v{s}}treits, Yuval Pinter, Cass Jacobs, ra L., Ryan Cotterell, Mans Hulden, David Yarowsky
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
1 code implementation • 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.
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.
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.
2 code implementations • ICLR 2019 • Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R. Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, Ellie Pavlick
The jiant toolkit for general-purpose text understanding models
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).
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.
no code implementations • ACL 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
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling.
3 code implementations • LREC 2018 • Christo Kirov, Ryan Cotterell, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia, Manaal Faruqui, Sabrina J. Mielke, Arya D. McCarthy, Sandra Kübler, David Yarowsky, Jason Eisner, Mans Hulden
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages.
1 code implementation • IJCNLP 2017 • Patrick Xia, David Yarowsky
We present a method which generates a single consensus between multi-parallel corpora.
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.
no code implementations • 2 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).