activity
20162023
most citedA User Simulator for Task-Completion Dialogues

138 citations · 480 across the 46 of their papers we have counts for

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Showing cs.CLShow all

42 papers · 1 filter

cs.CL2023★ 1 cited

CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation

Chao-Wei Huang, Chen-Yu Hsu, Tsu-Yuan Hsu +2

Conversational search provides a natural interface for information retrieval (IR). Recent approaches have demonstrated promising results in applying dense retrieval to conversation…

cs.CL2023

SalesBot 2.0: A Human-Like Intent-Guided Chit-Chat Dataset

Wen-Yu Chang, Yun-Nung Chen

In recent research on dialogue systems and corpora, there has been a significant focus on two distinct categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD sy…

cs.CL2023★ 13 cited

LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models

Yen-Ting Lin, Yun-Nung Chen

We propose LLM-Eval, a unified multi-dimensional automatic evaluation method for open-domain conversations with large language models (LLMs). Existing evaluation methods often rely…

cs.CL2022★ 4 cited

Open-Domain Conversational Question Answering with Historical Answers

Hung-Chieh Fang, Kuo-Han Hung, Chao-Wei Huang +1

Open-domain conversational question answering can be viewed as two tasks: passage retrieval and conversational question answering, where the former relies on selecting candidate pa…

cs.CL2022★ 1 cited

Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning

Hui-Chi Kuo, Yun-Nung Chen

Intelligent virtual assistants are currently designed to perform tasks or services explicitly mentioned by users, so multiple related domains or tasks need to be performed one by o…

cs.CL2022

Controllable User Dialogue Act Augmentation for Dialogue State Tracking

Chun-Mao Lai, Ming-Hao Hsu, Chao-Wei Huang +1

Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only co…

cs.CL2022

Islander: A Real-Time News Monitoring and Analysis System

Chao-Wei Huang, Kai-Chou Yang, Zi-Yuan Chen +8

With thousands of news articles from hundreds of sources distributed and shared every day, news consumption and information acquisition have been increasingly difficult for readers…

cs.CL2022★ 2 cited

SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues

Ssu Chiu, Maolin Li, Yen-Ting Lin +1

Dialogue systems are usually categorized into two types, open-domain and task-oriented. The first one focuses on chatting with users and making them engage in the conversations, wh…

cs.CL2022★ 5 cited

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…

cs.CL2020★ 4 cited

Adapting Pretrained Transformer to Lattices for Spoken Language Understanding

Chao-Wei Huang, Yun-Nung Chen

Lattices are compact representations that encode multiple hypotheses, such as speech recognition results or different word segmentations. It is shown that encoding lattices as oppo…

cs.CL2019★ 11 cited

Tree Transformer: Integrating Tree Structures into Self-Attention

Yau-Shian Wang, Hung-Yi Lee, Yun-Nung Chen

Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have achieved state-of-the-art results on diverse NLP tasks. However, it is unclear what the…

cs.CL2019

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…

cs.CL2019★ 1 cited

DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs

Yi-Lin Tuan, Yun-Nung Chen, Hung-yi Lee

Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-…

cs.CL2019

Learning ASR-Robust Contextualized Embeddings for Spoken Language Understanding

Chao-Wei Huang, Yun-Nung Chen

Employing pre-trained language models (LM) to extract contextualized word representations has achieved state-of-the-art performance on various NLP tasks. However, applying this tec…

cs.CL2019★ 2 cited

An Empirical Study of Content Understanding in Conversational Question Answering

Ting-Rui Chiang, Hao-Tong Ye, Yun-Nung Chen

With a lot of work about context-free question answering systems, there is an emerging trend of conversational question answering models in the natural language processing field. T…

cs.CL2019★ 7 cited

QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization

Yi-Ting Yeh, Yun-Nung Chen

Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets. However, the extent these systems…

cs.CL2019★ 18 cited

Entropy-Enhanced Multimodal Attention Model for Scene-Aware Dialogue Generation

Kuan-Yen Lin, Chao-Chun Hsu, Yun-Nung Chen +1

With increasing information from social media, there are more and more videos available. Therefore, the ability to reason on a video is important and deserves to be discussed. TheD…

cs.CL2019★ 13 cited

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…

cs.CL2019★ 10 cited

FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension

Yi-Ting Yeh, Yun-Nung Chen

Conversational machine comprehension requires deep understanding of the dialogue flow, and the prior work proposed FlowQA to implicitly model the context representations in reasoni…

cs.CL2019★ 6 cited

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…

cs.CL2019

Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation

Chia-Hsuan Lee, Yun-Nung Chen, Hung-Yi Lee

Spoken question answering (SQA) is challenging due to complex reasoning on top of the spoken documents. The recent studies have also shown the catastrophic impact of automatic spee…

cs.CL2019★ 3 cited

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…

cs.CL2019★ 1 cited

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…

cs.CL2019★ 3 cited

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…