Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Bao, Zuyi"'
This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this idea is that off-the-shelf pa
Externí odkaz:
http://arxiv.org/abs/2210.12484
Some grammatical error correction (GEC) systems incorporate hand-crafted rules and achieve positive results. However, manually defining rules is time-consuming and laborious. In view of this, we propose a method to mine error templates for GEC automa
Externí odkaz:
http://arxiv.org/abs/2206.11569
Autor:
Zhang, Yue, Li, Zhenghua, Bao, Zuyi, Li, Jiacheng, Zhang, Bo, Li, Chen, Huang, Fei, Zhang, Min
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7,063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources. Each sentence is cor
Externí odkaz:
http://arxiv.org/abs/2204.10994
Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the po
Externí odkaz:
http://arxiv.org/abs/2110.09915
Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-byword matching. Such requirements and assumptions are infeasible for most languages, especially for languages with large l
Externí odkaz:
http://arxiv.org/abs/1910.10893
Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment clas
Externí odkaz:
http://arxiv.org/abs/1908.04577
Publikováno v:
Natural Language Processing and Chinese Computing ISBN: 9783319995007
NLPCC (2)
NLPCC (2)
This paper introduces the Alibaba NLP team’s system for NLPCC 2018 shared task of Chinese Grammatical Error Correction (GEC). Chinese as a Second Language (CSL) learners can use this system to correct grammatical errors in texts they wrote. We prop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f42b5f47ac4b56f8d232df18b3150202
https://doi.org/10.1007/978-3-319-99501-4_10
https://doi.org/10.1007/978-3-319-99501-4_10
Publikováno v:
NLP-TEA@ACL
This paper introduces the DM_NLP team’s system for NLPTEA 2018 shared task of Chinese Grammatical Error Diagnosis (CGED), which can be used to detect and correct grammatical errors in texts written by Chinese as a Foreign Language (CFL) learners. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f1c4bd5f6368ad8e67fad2a1aa4dd31d
https://doi.org/10.18653/v1/w18-3708
https://doi.org/10.18653/v1/w18-3708
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