Search Results for author: Wenlin Yao

Found 9 papers, 4 papers with code

Weakly Supervised Subevent Knowledge Acquisition

no code implementations EMNLP 2020 Wenlin Yao, Zeyu Dai, Maitreyi Ramaswamy, Bonan Min, Ruihong Huang

We first obtain the initial set of event pairs that are likely to have the subevent relation, by exploiting two observations that 1) subevents are temporally contained by the parent event, and 2) the definitions of the parent event can be used to further guide the identification of subevents.

Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension

1 code implementation ACL 2022 Chao Zhao, Wenlin Yao, Dian Yu, Kaiqiang Song, Dong Yu, Jianshu Chen

Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations.

Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories

2 code implementations EMNLP 2021 Wenlin Yao, Xiaoman Pan, Lifeng Jin, Jianshu Chen, Dian Yu, Dong Yu

We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks.

Word Sense Disambiguation

Weakly-supervised Fine-grained Event Recognition on Social Media Texts for Disaster Management

1 code implementation4 Oct 2020 Wenlin Yao, Cheng Zhang, Shiva Saravanan, Ruihong Huang, Ali Mostafavi

People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management.


Temporal Event Knowledge Acquisition via Identifying Narratives

no code implementations ACL 2018 Wenlin Yao, Ruihong Huang

Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal "before/after" event knowledge across sentences in narrative stories.

General Classification Relation Classification +1

Using Context Events in Neural Network Models for Event Temporal Status Identification

no code implementations IJCNLP 2017 Zeyu Dai, Wenlin Yao, Ruihong Huang

Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts.

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