37 citations · 64 across the 15 of their papers we have counts for
8 papers · 1 filter
TaylorGAN: Neighbor-Augmented Policy Update for Sample-Efficient Natural Language Generation
Chun-Hsing Lin, Siang-Ruei Wu, Hung-Yi Lee +1
Score function-based natural language generation (NLG) approaches such as REINFORCE, in general, suffer from low sample efficiency and training instability problems. This is mainly…
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro +36
This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in…
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…
Toward Cross-Lingual Definition Generation for Language Learners
Cunliang Kong, Liner Yang, Tianzuo Zhang +4
Generating dictionary definitions automatically can prove useful for language learners. However, it's still a challenging task of cross-lingual definition generation. In this work,…
What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding
Yu-An Wang, Yun-Nung Chen
In recent years, pre-trained Transformers have dominated the majority of NLP benchmark tasks. Many variants of pre-trained Transformers have kept breaking out, and most focus on de…
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…
Learning Interpretable and Discrete Representations with Adversarial Training for Unsupervised Text Classification
Yau-Shian Wang, Hung-Yi Lee, Yun-Nung Chen
Learning continuous representations from unlabeled textual data has been increasingly studied for benefiting semi-supervised learning. Although it is relatively easier to interpret…