@inproceedings{muangkammuen-etal-2020-neural,
title = "A Neural Local Coherence Analysis Model for Clarity Text Scoring",
author = "Muangkammuen, Panitan and
Xu, Sheng and
Fukumoto, Fumiyo and
Runapongsa Saikaew, Kanda and
Li, Jiyi",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.194",
doi = "10.18653/v1/2020.coling-main.194",
pages = "2138--2143",
abstract = "Local coherence relation between two phrases/sentences such as cause-effect and contrast gives a strong influence of whether a text is well-structured or not. This paper follows the assumption and presents a method for scoring text clarity by utilizing local coherence between adjacent sentences. We hypothesize that the contextual features of coherence relations learned by utilizing different data from the target training data are also possible to discriminate well-structured of the target text and thus help to score the text clarity. We propose a text clarity scoring method that utilizes local coherence analysis with an out-domain setting, i.e. the training data for the source and target tasks are different from each other. The method with language model pre-training BERT firstly trains the local coherence model as an auxiliary manner and then re-trains it together with clarity text scoring model. The experimental results by using the PeerRead benchmark dataset show the improvement compared with a single model, scoring text clarity model. Our source codes are available online.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="muangkammuen-etal-2020-neural">
<titleInfo>
<title>A Neural Local Coherence Analysis Model for Clarity Text Scoring</title>
</titleInfo>
<name type="personal">
<namePart type="given">Panitan</namePart>
<namePart type="family">Muangkammuen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sheng</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fumiyo</namePart>
<namePart type="family">Fukumoto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kanda</namePart>
<namePart type="family">Runapongsa Saikaew</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiyi</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 28th International Conference on Computational Linguistics</title>
</titleInfo>
<originInfo>
<publisher>International Committee on Computational Linguistics</publisher>
<place>
<placeTerm type="text">Barcelona, Spain (Online)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Local coherence relation between two phrases/sentences such as cause-effect and contrast gives a strong influence of whether a text is well-structured or not. This paper follows the assumption and presents a method for scoring text clarity by utilizing local coherence between adjacent sentences. We hypothesize that the contextual features of coherence relations learned by utilizing different data from the target training data are also possible to discriminate well-structured of the target text and thus help to score the text clarity. We propose a text clarity scoring method that utilizes local coherence analysis with an out-domain setting, i.e. the training data for the source and target tasks are different from each other. The method with language model pre-training BERT firstly trains the local coherence model as an auxiliary manner and then re-trains it together with clarity text scoring model. The experimental results by using the PeerRead benchmark dataset show the improvement compared with a single model, scoring text clarity model. Our source codes are available online.</abstract>
<identifier type="citekey">muangkammuen-etal-2020-neural</identifier>
<identifier type="doi">10.18653/v1/2020.coling-main.194</identifier>
<location>
<url>https://aclanthology.org/2020.coling-main.194</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>2138</start>
<end>2143</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Neural Local Coherence Analysis Model for Clarity Text Scoring
%A Muangkammuen, Panitan
%A Xu, Sheng
%A Fukumoto, Fumiyo
%A Runapongsa Saikaew, Kanda
%A Li, Jiyi
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F muangkammuen-etal-2020-neural
%X Local coherence relation between two phrases/sentences such as cause-effect and contrast gives a strong influence of whether a text is well-structured or not. This paper follows the assumption and presents a method for scoring text clarity by utilizing local coherence between adjacent sentences. We hypothesize that the contextual features of coherence relations learned by utilizing different data from the target training data are also possible to discriminate well-structured of the target text and thus help to score the text clarity. We propose a text clarity scoring method that utilizes local coherence analysis with an out-domain setting, i.e. the training data for the source and target tasks are different from each other. The method with language model pre-training BERT firstly trains the local coherence model as an auxiliary manner and then re-trains it together with clarity text scoring model. The experimental results by using the PeerRead benchmark dataset show the improvement compared with a single model, scoring text clarity model. Our source codes are available online.
%R 10.18653/v1/2020.coling-main.194
%U https://aclanthology.org/2020.coling-main.194
%U https://doi.org/10.18653/v1/2020.coling-main.194
%P 2138-2143
Markdown (Informal)
[A Neural Local Coherence Analysis Model for Clarity Text Scoring](https://aclanthology.org/2020.coling-main.194) (Muangkammuen et al., COLING 2020)
ACL
- Panitan Muangkammuen, Sheng Xu, Fumiyo Fukumoto, Kanda Runapongsa Saikaew, and Jiyi Li. 2020. A Neural Local Coherence Analysis Model for Clarity Text Scoring. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2138–2143, Barcelona, Spain (Online). International Committee on Computational Linguistics.