A Neural Local Coherence Analysis Model for Clarity Text Scoring

Proceedings of the 28th International Conference on Computational Linguistics, pages 2138–2143 Barcelona, Spain (Online), December 8-13, 2020 2138 A Neural Local Coherence Analysis Model for Text Clarity Scoring Panitan Muangkammuen1, Sheng Xu1,2, Fumiyo Fukumoto1, Kanda Runapongsa Saikaew3, Jiyi Li1 1University of Yamanashi {g19tk021,g18tka07,fukumoto,jyli}@yamanashi.ac.jp 2Hangzhou Dianzi University 3Khon Kaen University krunapon@kku.ac.th Abstract Local coherence relation between two phrases/sentences, such as cause-effect, and contrast gives a strong influence on 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 coherence knowledge learned from the different domain data is beneficial for capturing a well-structured text and thus helpful for scoring 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 based on the pre-trained language model BERT firstly trains the local coherence model as an auxiliary manner and then re-trains it together with the text clarity scoring model. The experimental results by using the PeerRead benchmark dataset show the improvement compared with a single model, scoring text clarity model1. 1 Introduction Text clarity scoring can be defined as a task that assesses well-structured in a text by grading. It is bene- ficial for not only authors/reviewers of scholarly papers but also students to improve their writing skills. Among several properties such as spelling, grammar, and word choice, local coherence, which captures text relatedness at the level of sentence-to-sentence transitions (Barzilay and Lapata, 2008), is one of the main properties to identify whether a text is well-structured or not. Well-known early attempts for modeling coherence are lexical coherence models (Halliday and Hasan, 1976), psychological models of discourse (Foltz et al., 1998), rhetorical structure theory (Mann and Thompson, 1998), lexical chains (Morris and Hirst, 1991), and entity grid model (Barzilay and Lapata, 2008). More recently, the co- herence model based on deep learning techniques has been intensively studied. These attempts include recursive and recurrent neural networks (Li and Hovy, 2014; Li and Jurafsky, 2017), a combination of LSTM and CNN (Mesgar and Strube, 2018), a deep coherence model based on CNN (Cui et al., 2017), SKIPFLOW LSTM (Tay et al., 2018), and a pre-trained generative model (Xu et al., 2019). It enables to encode patterns of semantic changes in a text. Despite some successes, techniques explored so far mainly rely on word sequence within a sentence. Farag and Yannakoudakis (2019) attempted to encode information about the types of grammatical roles, such as clausal modifiers of nouns and coordinating conjunction in a sentence obtained by using the Stanford Dependency Parser (Chen and Manning, 2014). for coherence modeling. The authors utilize a hierarchical neural network model trained two tasks, grammatical roles, and coherence in a multi-task manner. In this paper, we propose a method for text clarity scoring by utilizing a coherence model as an auxiliary manner. Instead of training grammatical …

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