Search Results for author: Shang-Yu Su

Found 21 papers, 12 papers with code

TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues

no code implementations31 Aug 2021 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.

Relation Extraction

Dual Inference for Improving Language Understanding and Generation

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.

Natural Language Understanding Text Generation

Lifelong Language Knowledge Distillation

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.

Knowledge Distillation Language Modelling +2

Towards Unsupervised Language Understanding and Generation by Joint Dual Learning

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.

Natural Language Understanding Text Generation

Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling

no code implementations14 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.

Question Answering Scene-Aware Dialogue +2

HUMBO: Bridging Response Generation and Facial Expression Synthesis

no code implementations24 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.

Dialogue Generation Natural Language Processing +2

Dual Supervised Learning for Natural Language Understanding and Generation

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.

Natural Language Understanding Text Generation

Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model

no code implementations23 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.

Dialogue Generation Response Generation

RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection

no code implementations21 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.

Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer

no code implementations21 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.

Response Generation

Modeling Melodic Feature Dependency with Modularized Variational Auto-Encoder

no code implementations31 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.

Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation

1 code implementation19 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.

Text Generation

Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

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.

Task-Completion Dialogue Policy Learning

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

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.

Task-Completion Dialogue Policy Learning

Dynamic Time-Aware Attention to Speaker Roles and Contexts for Spoken Language Understanding

1 code implementation30 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.

Dialogue State Tracking Spoken Language Understanding

Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning

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.

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