Papers by Chien-Wei Lin
Few Shot Dialogue State Tracking using Meta-learning (2021.eacl-main)
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| Challenge: | Existing methods for transferring knowledge from resource-rich domains to unknown domains are data hungry . a meta-learning algorithm is proposed to solve the problem of zero/few-shot DST . |
| Approach: | They propose a meta-learner for the problem of zero/few-shot DST . they propose to agnostically train any existing chatbot system to improve its performance . |
| Outcome: | The proposed meta-learner improves on baseline in a low-data setting. |
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution (2022.coling-1)
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Danfeng Guo, Arpit Gupta, Sanchit Agarwal, Jiun-Yu Kao, Shuyang Gao, Arijit Biswas, Chien-Wei Lin, Tagyoung Chung, Mohit Bansal
| Challenge: | Multimodal coreference resolution (MCR) is a crucial capability for building next-generation conversational agents. |
| Approach: | They propose a multimodal coreference resolution model that resolves coreferences made in multi-turn dialogues with scene images. |
| Outcome: | The proposed model resolves coreferences made in multi-turn dialogues with scene images. |
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)
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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
| Challenge: | Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability. |
| Approach: | They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations . |
| Outcome: | The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient . |