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20202022
most citedRetrofitting Structure-aware Transformer Language Model for End Tasks

22 citations · 29 across the 5 of their papers we have counts for

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cs.CL202385 cited

On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

Hao Fei, Tat-Seng Chua, Chenliang Li +3

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media text…

cs.CL2022

Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph

Hao Fei, Shengqiong Wu, Meishan Zhang +2

Conversational semantic role labeling (CSRL) is a newly proposed task that uncovers the shallow semantic structures in a dialogue text. Unfortunately several important characterist…

cs.CL2021

Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge

Shengqiong Wu, Hao Fei, Yafeng Ren +2

In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encode…

cs.CL2020

Nominal Compound Chain Extraction: A New Task for Semantic-enriched Lexical Chain

Bobo Li, Hao Fei, Yafeng Ren +1

Lexical chain consists of cohesion words in a document, which implies the underlying structure of a text, and thus facilitates downstream NLP tasks. Nevertheless, existing work foc…

cs.CL20203 cited

Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP

Hao Fei, Yafeng Ren, Donghong Ji

Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network. In this paper, we investigate…

cs.CL202022 cited

Retrofitting Structure-aware Transformer Language Model for End Tasks

Hao Fei, Yafeng Ren, Donghong Ji

We consider retrofitting structure-aware Transformer-based language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constitue…