44 citations · 125 across the 19 of their papers we have counts for
19 papers
DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever
Zhichao Yin, Binyuan Hui, Min Yang +2
Recently, substantial advancements in pre-trained vision-language models have greatly enhanced the capabilities of multi-modal dialog systems. These models have demonstrated signif…
Unifying Structured Data as Graph for Data-to-Text Pre-Training
Shujie Li, Liang Li, Ruiying Geng +8
Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yiel…
Constructive Large Language Models Alignment with Diverse Feedback
Tianshu Yu, Ting-En Lin, Yuchuan Wu +3
In recent research on large language models (LLMs), there has been a growing emphasis on aligning these models with human values to reduce the impact of harmful content. However, c…
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models
Haoyu Gao, Ting-En Lin, Hangyu Li +4
Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these…
VDialogUE: A Unified Evaluation Benchmark for Visually-grounded Dialogue
Yunshui Li, Binyuan Hui, Zhaochao Yin +6
Visually-grounded dialog systems, which integrate multiple modes of communication such as text and visual inputs, have become an increasingly popular area of investigation. However…
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
Yunshui Li, Binyuan Hui, ZhiChao Yin +3
Perceiving multi-modal information and fulfilling dialogues with humans is a long-term goal of artificial intelligence. Pre-training is commonly regarded as an effective approach f…
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
Tianshu Yu, Haoyu Gao, Ting-En Lin +6
Recently, speech-text pre-training methods have shown remarkable success in many speech and natural language processing tasks. However, most previous pre-trained models are usually…
Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation
Haoyu Gao, Rui Wang, Ting-En Lin +4
Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks. Previous DTS methods either focus on semantic similarity or dialogue coherence to…
Iterative Forward Tuning Boosts In-Context Learning in Language Models
Jiaxi Yang, Binyuan Hui, Min Yang +5
Despite the advancements in in-context learning (ICL) for large language models (LLMs), current research centers on specific prompt engineering, such as demonstration selection, wi…
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
Yunhu Ye, Binyuan Hui, Min Yang +3
Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions a…
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
Zefeng Cai, Xiangyu Li, Binyuan Hui +8
In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural languag…
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
Bowen Qin, Lihan Wang, Binyuan Hui +7
This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncert…
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
Wanwei He, Yinpei Dai, Min Yang +4
Recently, pre-training methods have shown remarkable success in task-oriented dialog (TOD) systems. However, most existing pre-trained models for TOD focus on either dialog underst…
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding
Wanwei He, Yinpei Dai, Binyuan Hui +6
Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks. However, current contrastive learning solely considers the se…
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
Bowen Qin, Binyuan Hui, Lihan Wang +9
Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is to convert a natural language (NL) question to its corresponding structured query langu…
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
Lihan Wang, Bowen Qin, Binyuan Hui +7
The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., p…
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Wanwei He, Yinpei Dai, Yinhe Zheng +9
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and ge…
Linking-Enhanced Pre-Training for Table Semantic Parsing
Bowen Qin, Lihan Wang, Binyuan Hui +5
Recently pre-training models have significantly improved the performance of various NLP tasks by leveraging large-scale text corpora to improve the contextual representation abilit…
A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems
Dingmin Wang, Ziyao Chen, Wanwei He +3
Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of wo…