no code implementations • 27 Mar 2022 • Ting-Chun Wang, Shang-Yu Su, Yun-Nung Chen
CRS is a complex problem that consists of two main tasks: (1) recommendation and (2) response generation.
1 code implementation • SIGDIAL (ACL) 2022 • Po-Wei Lin, Shang-Yu Su, Yun-Nung Chen
The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Shang-Yu Su, Yung-Sung Chuang, Yun-Nung Chen
Natural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does the opposite.
1 code implementation • EMNLP 2020 • Yung-Sung Chuang, Shang-Yu Su, Yun-Nung Chen
It is challenging to perform lifelong language learning (LLL) on a stream of different tasks without any performance degradation comparing to the multi-task counterparts.
1 code implementation • ACL 2020 • Shang-Yu Su, Chao-Wei Huang, Yun-Nung Chen
The prior work is the first attempt that utilized the duality between NLU and NLG to improve the performance via a dual supervised learning framework.
1 code implementation • WS 2019 • Alexander Te-Wei Shieh, Yung-Sung Chuang, Shang-Yu Su, Yun-Nung Chen
We first build a pointer-generator baseline model for conclusion generation.
no code implementations • 14 Aug 2019 • Yi-Ting Yeh, Tzu-Chuan Lin, Hsiao-Hua Cheng, Yu-Hsuan Deng, Shang-Yu Su, Yun-Nung Chen
Visual question answering and visual dialogue tasks have been increasingly studied in the multimodal field towards more practical real-world scenarios.
no code implementations • 24 May 2019 • Shang-Yu Su, Po-Wei Lin, Yun-Nung Chen
Spoken dialogue systems that assist users to solve complex tasks such as movie ticket booking have become an emerging research topic in artificial intelligence and natural language processing areas.
2 code implementations • ACL 2019 • Shang-Yu Su, Chao-Wei Huang, Yun-Nung Chen
Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP field.
no code implementations • 23 Mar 2019 • Hao-Tong Ye, Kai-Ling Lo, Shang-Yu Su, Yun-Nung Chen
End-to-end dialogue generation has achieved promising results without using handcrafted features and attributes specific for each task and corpus.
no code implementations • 21 Mar 2019 • Chao-Wei Huang, Ting-Rui Chiang, Shang-Yu Su, Yun-Nung Chen
The response selection has been an emerging research topic due to the growing interest in dialogue modeling, where the goal of the task is to select an appropriate response for continuing dialogues.
no code implementations • 21 Mar 2019 • Ting-Rui Chiang, Chao-Wei Huang, Shang-Yu Su, Yun-Nung Chen
With the increasing research interest in dialogue response generation, there is an emerging branch formulating this task as selecting next sentences, where given the partial dialogue contexts, the goal is to determine the most probable next sentence.
no code implementations • 31 Oct 2018 • Yu-An Wang, Yu-Kai Huang, Tzu-Chuan Lin, Shang-Yu Su, Yun-Nung Chen
Automatic melody generation has been a long-time aspiration for both AI researchers and musicians.
1 code implementation • 19 Sep 2018 • Shang-Yu Su, Yun-Nung Chen
Natural language generation (NLG) is a critical component in spoken dialogue system, which can be divided into two phases: (1) sentence planning: deciding the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string.
no code implementations • 5 Sep 2018 • Shang-Yu Su, Pei-Chieh Yuan, Yun-Nung Chen
Spoken language understanding (SLU) is an essential component in conversational systems.
3 code implementations • EMNLP 2018 • Shang-Yu Su, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen
This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning.
1 code implementation • NAACL 2018 • Shang-Yu Su, Kai-Ling Lo, Yi-Ting Yeh, Yun-Nung Chen
Natural language generation (NLG) is a critical component in spoken dialogue systems.
1 code implementation • NAACL 2018 • Shang-Yu Su, Pei-Chieh Yuan, Yun-Nung Chen
Spoken language understanding (SLU) is an essential component in conversational systems.
3 code implementations • ACL 2018 • Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong, Shang-Yu Su
During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience.
Reinforcement Learning (RL) Task-Completion Dialogue Policy Learning
1 code implementation • 30 Sep 2017 • Po-Chun Chen, Ta-Chung Chi, Shang-Yu Su, Yun-Nung Chen
However, the previous model only paid attention to the content in history utterances without considering their temporal information and speaker roles.
1 code implementation • IJCNLP 2017 • Ta-Chung Chi, Po-Chun Chen, Shang-Yu Su, Yun-Nung Chen
Language understanding (LU) and dialogue policy learning are two essential components in conversational systems.