most citedA Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution

7 citations · 14 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CL20207 cited

Understanding Pre-trained BERT for Aspect-based Sentiment Analysis

Hu Xu, Lei Shu, Philip S. Yu +1

This paper analyzes the pre-trained hidden representations learned from reviews on BERT for tasks in aspect-based sentiment analysis (ABSA). Our work is motivated by the recent pro…

cs.CL2020

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions

Zhiyu Chen, Honglei Liu, Hu Xu +3

Existing conversational systems are mostly agent-centric, which assumes the user utterances would closely follow the system ontology (for NLU or dialogue state tracking). However,…

cs.CL2020

Controllable Text Generation with Focused Variation

Lei Shu, Alexandros Papangelis, Yi-Chia Wang +5

This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from t…

cs.CL20197 cited

A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution

Hu Xu, Bing Liu, Lei Shu +1

Aspect-based sentiment classification (ASC) is an important task in fine-grained sentiment analysis.~Deep supervised ASC approaches typically model this task as a pair-wise classif…

cs.CL2019

Modeling Multi-Action Policy for Task-Oriented Dialogues

Lei Shu, Hu Xu, Bing Liu +1

Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only o…

cs.CL2019

Flexibly-Structured Model for Task-Oriented Dialogues

Lei Shu, Piero Molino, Mahdi Namazifar +4

This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, wher…