Search Results for author: YIlun Zhu

Found 15 papers, 8 papers with code

Can Large Language Models Understand Context?

no code implementations1 Feb 2024 YIlun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng

Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.

In-Context Learning Quantization

Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization

1 code implementation20 Sep 2023 YIlun Zhu, Siyao Peng, Sameer Pradhan, Amir Zeldes

Previous attempts to incorporate a mention detection step into end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention span data as well as other entity information.

coreference-resolution Multi-Task Learning

GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation

1 code implementation3 Jun 2023 Tatsuya Aoyama, Shabnam Behzad, Luke Gessler, Lauren Levine, Jessica Lin, Yang Janet Liu, Siyao Peng, YIlun Zhu, Amir Zeldes

We evaluate state-of-the-art NLP systems on GENTLE and find severe degradation for at least some genres in their performance on all tasks, which indicates GENTLE's utility as an evaluation dataset for NLP systems.

coreference-resolution Dependency Parsing +2

Mixture Proportion Estimation Beyond Irreducibility

1 code implementation2 Jun 2023 YIlun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, Clayton Scott

The task of mixture proportion estimation (MPE) is to estimate the weight of a component distribution in a mixture, given observations from both the component and mixture.

AMALGUM -- A Free, Balanced, Multilayer English Web Corpus

1 code implementation LREC 2020 Luke Gessler, Siyao Peng, Yang Liu, YIlun Zhu, Shabnam Behzad, Amir Zeldes

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory.


A Corpus of Adpositional Supersenses for Mandarin Chinese

no code implementations LREC 2020 Siyao Peng, Yang Liu, YIlun Zhu, Austin Blodgett, Yushi Zhao, Nathan Schneider

Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language.


Adpositional Supersenses for Mandarin Chinese

no code implementations6 Dec 2018 YIlun Zhu, Yang Liu, Siyao Peng, Austin Blodgett, Yushi Zhao, Nathan Schneider

This study adapts Semantic Network of Adposition and Case Supersenses (SNACS) annotation to Mandarin Chinese and demonstrates that the same supersense categories are appropriate for Chinese adposition semantics.

Machine Translation Translation

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