19 citations · 85 across the 20 of their papers we have counts for
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BARCOR: Towards A Unified Framework for Conversational Recommendation Systems
Ting-Chun Wang, Shang-Yu Su, Yun-Nung Chen
Recommendation systems focus on helping users find items of interest in the situations of information overload, where users' preferences are typically estimated by the past observe…
TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues
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. During conversations, speakers may expose their relations to ce…
Dual Inference for Improving Language Understanding and Generation
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 lang…
Lifelong Language Knowledge Distillation
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. To addres…
Towards Unsupervised Language Understanding and Generation by Joint Dual Learning
Shang-Yu Su, Chao-Wei Huang, Yun-Nung Chen
In modular dialogue systems, natural language understanding (NLU) and natural language generation (NLG) are two critical components, where NLU extracts the semantics from the given…
Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation
Alexander Te-Wei Shieh, Yung-Sung Chuang, Shang-Yu Su +1
Randomized controlled trials (RCTs) represent the paramount evidence of clinical medicine. Using machines to interpret the massive amount of RCTs has the potential of aiding clinic…
Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling
Yi-Ting Yeh, Tzu-Chuan Lin, Hsiao-Hua Cheng +3
Visual question answering and visual dialogue tasks have been increasingly studied in the multimodal field towards more practical real-world scenarios. A more challenging task, aud…
HUMBO: Bridging Response Generation and Facial Expression Synthesis
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…
Dual Supervised Learning for Natural Language Understanding and Generation
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. Natural language understanding is to extract the core…
Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model
Hao-Tong Ye, Kai-Ling Lo, Shang-Yu Su +1
End-to-end dialogue generation has achieved promising results without using handcrafted features and attributes specific for each task and corpus. However, one of the fatal drawbac…
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
Ting-Rui Chiang, Chao-Wei Huang, Shang-Yu Su +1
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 dialog…
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection
Chao-Wei Huang, Ting-Rui Chiang, Shang-Yu Su +1
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 con…
Dynamically Context-Sensitive Time-Decay Attention for Dialogue Modeling
Shang-Yu Su, Pei-Chieh Yuan, Yun-Nung Chen
Spoken language understanding (SLU) is an essential component in conversational systems. Considering that contexts provide informative cues for better understanding, history can be…
Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
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 stru…
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
Shang-Yu Su, Xiujun Li, Jianfeng Gao +2
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 D…
Natural Language Generation by Hierarchical Decoding with Linguistic Patterns
Shang-Yu Su, Kai-Ling Lo, Yi-Ting Yeh +1
Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sen…
Dynamic Time-Aware Attention to Speaker Roles and Contexts for Spoken Language Understanding
Po-Chun Chen, Ta-Chung Chi, Shang-Yu Su +1
Spoken language understanding (SLU) is an essential component in conversational systems. Most SLU component treats each utterance independently, and then the following components a…
Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning
Ta-Chung Chi, Po-Chun Chen, Shang-Yu Su +1
Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and…