Yun-Nung Chen

Intra-Speaker Topic Modeling for Improved Multi-Party Meeting Summarization with Integrated Random Walk

2018 · INDIGO (University of Illinois at Chicago)

This paper proposes an improved approach of summarization for spoken multi-party interaction, in which integrated random walk is performed on a graph constructed with topical/lexical relations. Each utterance is represented as a node of the …

Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation

2018 · arXiv (Cornell University)

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 …

End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager

2016 · arXiv (Cornell University)

Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding …

Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification

2017

Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition.The central ideas of CTC include adding a label "blank" during training.With this mechanism, CTC eliminates the …

Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

2017

Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.

Dual Inference for Improving Language Understanding and Generation

2020 · arXiv (Cornell University)

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. The prior work …

Zero-shot learning of intent embeddings for expansion by convolutional deep structured semantic models

2016

The recent surge of intelligent personal assistants motivates spoken language understanding of dialogue systems. However, the domain constraint along with the inflexible intent schema remains a big issue. This paper focuses on the task of …

End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding

2016

Spoken language understanding (SLU) is a core component of a spoken dialogue system. In the traditional architecture of dialogue systems, the SLU component treats each utterance independent of each other, and then the following components …

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

2018

Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, Yun-Nung Chen. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 …

أوراق هذا المؤلف