416 citations · 1.6k across the 85 of their papers we have counts for
15 papers · 2 filters
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…
EventBERT: A Pre-Trained Model for Event Correlation Reasoning
Yucheng Zhou, Xiubo Geng, Tao Shen +2
Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense. For example, "Andrew was very drowsy, so he took…
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…
Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning
Chongyang Tao, Jiazhan Feng, Chang Liu +3
Establishing retrieval-based dialogue systems that can select appropriate responses from the pre-built index has gained increasing attention from researchers. For this task, the ad…
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
Yingmei Guo, Linjun Shou, Jian Pei +4
Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have be…
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…
Maria: A Visual Experience Powered Conversational Agent
Zujie Liang, Huang Hu, Can Xu +5
Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus pro…
Language Scaling for Universal Suggested Replies Model
Qianlan Ying, Payal Bajaj, Budhaditya Deb +7
We consider the problem of scaling automated suggested replies for Outlook email system to multiple languages. Faced with increased compute requirements and low resources for langu…
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling +3
Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification…
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
Shining Liang, Ming Gong, Jian Pei +4
Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performan…
CoSQA: 20,000+ Web Queries for Code Search and Question Answering
Junjie Huang, Duyu Tang, Linjun Shou +5
Finding codes given natural language query isb eneficial to the productivity of software developers. Future progress towards better semantic matching between query and code require…
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text
Siyuan Wang, Wanjun Zhong, Duyu Tang +5
Logical reasoning of text requires understanding critical logical information in the text and performing inference over them. Large-scale pre-trained models for logical reasoning m…
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach
Junjie Huang, Duyu Tang, Wanjun Zhong +5
Producing the embedding of a sentence in an unsupervised way is valuable to natural language matching and retrieval problems in practice. In this work, we conduct a thorough examin…
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
Weizhen Qi, Yeyun Gong, Yu Yan +9
Now, the pre-training technique is ubiquitous in natural language processing field. ProphetNet is a pre-training based natural language generation method which shows powerful perfo…
Integrating Pre-trained Model into Rule-based Dialogue Management
Jun Quan, Meng Yang, Qiang Gan +9
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to m…