53 citations · 78 across the 13 of their papers we have counts for
13 papers
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
Qingfeng Sun, Can Xu, Huang Hu +6
Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in producing rational and factual responses. However, to establish long-term relationships with users, the KD…
FORCE: A Framework of Rule-Based Conversational Recommender System
Jun Quan, Ze Wei, Qiang Gan +11
The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which ar…
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
Yufei Wang, Can Xu, Qingfeng Sun +4
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model (PromDA) which only trains…
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao +5
Generating natural and informative texts has been a long-standing problem in NLP. Much effort has been dedicated into incorporating pre-trained language models (PLMs) with various…
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations
Jia-Chen Gu, Chao-Hong Tan, Chongyang Tao +4
Recently, various response generation models for two-party conversations have achieved impressive improvements, but less effort has been paid to multi-party conversations (MPCs) wh…
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
Yuntao Li, Can Xu, Huang Hu +3
Retrieve-based dialogue response selection aims to find a proper response from a candidate set given a multi-turn context. Pre-trained language models (PLMs) based methods have yie…
Multimodal Dialogue Response Generation
Qingfeng Sun, Yujing Wang, Can Xu +7
Responsing with image has been recognized as an important capability for an intelligent conversational agent. Yet existing works only focus on exploring the multimodal dialogue mod…
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models
Lingzhi Wang, Huang Hu, Lei Sha +3
Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS i…
Learning Neural Templates for Recommender Dialogue System
Zujie Liang, Huang Hu, Can Xu +6
Though recent end-to-end neural models have shown promising progress on Conversational Recommender System (CRS), two key challenges still remain. First, the recommended items canno…
Re-entry Prediction for Online Conversations via Self-Supervised Learning
Lingzhi Wang, Xingshan Zeng, Huang Hu +2
In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the dis…
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations
Wei Chen, Yeyun Gong, Can Xu +9
We study the problem of coarse-grained response selection in retrieval-based dialogue systems. The problem is equally important with fine-grained response selection, but is less ex…
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Yufei Wang, Can Xu, Huang Hu +5
Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language gen…
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
Ruozi Huang, Huang Hu, Wei Wu +3
Dancing to music is one of human's innate abilities since ancient times. In machine learning research, however, synthesizing dance movements from music is a challenging problem. Re…