Justin Chih-Yao Chen

Selected Publications

1. Preprint

   MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models

   Chen, Justin Chih-Yao, Saha, Swarnadeep, Stengel-Eskin, Elias, and Bansal, Mohit

   2024

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2. Preprint

   ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

   Chen, Justin Chih-Yao, Saha, Swarnadeep, and Bansal, Mohit

   2023

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3. EMNLP’23

   Location-Aware Visual Question Generation with Lightweight Models

   Suwono, Nicholas Collin, Chen, Justin Chih-Yao, Hung, Tun Min, Huang, Ting-Hao Kenneth, Liao, I-Bin, Li, Yung-Hui, Ku, Lun-Wei, and Sun, Shao-Hua

   2023

   PDF
4. ACL’23

   HonestBait: Forward References for Attractive but Faithful Headline Generation

   In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2023

   PDF BibTeX Video
5. ACL’23

   Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification

   In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2023

   PDF BibTeX Video
6. NAACL’21

   ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning

   Chen, Chih-Yao, and Li, Cheng-Te

   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2021

   Abs PDF BibTeX Video

   While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage. In this paper, we formulate the zero-shot relation extraction problem by incorporating the text description of seen and unseen relations. We propose a novel multi-task learning model, Zero-Shot BERT (ZS-BERT), to directly predict unseen relations without hand-crafted attribute labeling and multiple pairwise classifications. Given training instances consisting of input sentences and the descriptions of their seen relations, ZS-BERT learns two functions that project sentences and relations into an embedding space by jointly minimizing the distances between them and classifying seen relations. By generating the embeddings of unseen relations and new-coming sentences based on such two functions, we use nearest neighbor search to obtain the prediction of unseen relations. Experiments conducted on two well-known datasets exhibit that ZS-BERT can outperform existing methods by at least 13.54% improvement on F1 score.