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20162021
most citedCode-Switching for Enhancing NMT with Pre-Specified Translation

43 citations · 144 across the 24 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CL2021

Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph

Tong Zhu, Xiaoye Qu, Wenliang Chen +4

Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in b…

cs.CL2021★ 9 cited

Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments

Yu Zhang, Qingrong Xia, Shilin Zhou +3

Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiqui…

cs.CL2021

Exploiting Rich Syntax for Better Knowledge Base Question Answering

Pengju Zhang, Yonghui Jia, Muhua Zhu +2

Recent studies on Knowledge Base Question Answering (KBQA) have shown great progress on this task via better question understanding. Previous works for encoding questions mainly fo…

cs.CL2021★ 2 cited

Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction

Jinpeng Zhang, Baijun Ji, Nini Xiao +4

Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word…

cs.CL2021★ 3 cited

Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots

Juntao Li, Chang Liu, Chongyang Tao +4

Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context uttera…

cs.CL2021★ 3 cited

Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing

Kun Wu, Lijie Wang, Zhenghua Li +5

Data augmentation has attracted a lot of research attention in the deep learning era for its ability in alleviating data sparseness. The lack of labeled data for unseen evaluation…