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20192023
most citedA Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer

41 citations · 85 across the 23 of their papers we have counts for

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24 papers · 1 filter

cs.CL2023

Guiding AMR Parsing with Reverse Graph Linearization

Bofei Gao, Liang Chen, Peiyi Wang +2

Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic gr…

cs.CL2023

Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting

Shuzheng Si, Shuang Zeng, Baobao Chang

Incomplete utterance rewriting has recently raised wide attention. However, previous works do not consider the semantic structural information between incomplete utterance and rewr…

cs.CL20221 cited

Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling

Ce Zheng, Yiming Wang, Baobao Chang

Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with frame semantic roles defined in FrameNet. Previous researches tend to divide FSRL into argument identi…

cs.CL2022

A Two-Stage Method for Chinese AMR Parsing

Liang Chen, Bofei Gao, Baobao Chang

In this paper, we provide a detailed description of our system at CAMRP-2022 evaluation. We firstly propose a two-stage method to conduct Chinese AMR Parsing with alignment generat…

cs.CL20223 cited

Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances

Shoujie Tong, Qingxiu Dong, Damai Dai +4

Fine-tuning pretrained language models (PLMs) on downstream tasks has become common practice in natural language processing. However, most of the PLMs are vulnerable, e.g., they ar…

cs.CL2022

A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction

Runxin Xu, Peiyi Wang, Tianyu Liu +3

Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting eve…