A Neural Local Coherence Analysis Model for Clarity Text Scoring
Panitan Muangkammuen, Sheng Xu, Fumiyo Fukumoto, Kanda Runapongsa Saikaew, Jiyi Li · 2020
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 model 1 .