53 citations · 78 across the 13 of their papers we have counts for
7 papers · 1 filter
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…