activity
20192021
most citedAn End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification

18 citations · 18 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2021

A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification

Guang Liu, Hailong Huang, Yuzhao Mao +3

Data augmentation (DA) aims to generate constrained and diversified data to improve classifiers in Low-Resource Classification (LRC). Previous studies mostly use a fine-tuned Langu…

cs.CL2021

Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup

Guang Liu, Yuzhao Mao, Hailong Huang +2

Mixup is a recent regularizer for current deep classification networks. Through training a neural network on convex combinations of pairs of examples and their labels, it imposes l…

cs.CL2020

DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation

Yuzhao Mao, Qi Sun, Guang Liu +4

Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems. Existing studies on ERC primarily focus on summarizing the context informatio…

cs.AI2020

Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion

Jingyuan Yang, Guang Liu, Yuzhao Mao +5

Task 1 of the DSTC8-track1 challenge aims to develop an end-to-end multi-domain dialogue system to accomplish complex users' goals under tourist information desk settings. This pap…

cs.CL201918 cited

An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification

Yue Ma, Zengfeng Zeng, Dawei Zhu +5

This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue State Tracking. The goal of this task is to predict the intents and slots in each user turn to complete…