Parallel Corpus Filtering for J apanese Text Simplification (2022)

Abstract: We propose a method of parallel corpus filtering for Japanese text simplification. The parallel corpus for this task contains some redundant wording. In this study, we first identify the type and size of noisy sentence pairs in the Japanese text simplification corpus. We then propose a method of parallel corpus filtering to remove each type of noisy sentence pair. Experimental results show that filtering the training parallel corpus with the proposed method improves simplification performance.

## introduction the number of foreign residents in japan is increasing yearly due to government policies and the progress of globalization. @xcite reported that many of them can partially understand japanese, more than the number who can understand other languages such as english or chinese. therefore, many information such as disaster information and daily news @xcite are provided in "easy japanese" in japan today. recent research in text simplification has focused on data-driven approaches @xcite based on parallel corpora @xcite @xcite . for japanese, a parallel corpus with tens of thousands of sentence pairs @xcite is available for the study of text simplification. however, the japanese text simplification corpus contains 16% of noisy sentence pairs, as shown in in this study, we first identify the type and size of noisy sentence pairs in the japanese text simplification corpus. as shown in ## related work parallel corpus filtering @xcite ) is a technique that has been studied primarily in machine translation tasks where a large parallel training corpus is available, and contributes to improving the quality of the generated text by removing noisy sentence pairs from the training data. text simplification tasks use relatively small training data. therefore, parallel corpus mining @xcite @xcite has been actively studied, but there is no prior research on parallel corpus filtering for text simplification. ## methodology we propose a method of parallel corpus filtering for noise in japanese simplification corpus @xcite to improve the performance of japanese text simplification. parallel corpus filtering is performed on the training data using multiple methods, and the resulting subset of training data is used to train the text simplification model. transformer @xcite and pre-trained bart @xcite are used for the model, and a text simplification model is constructed by fine-tuning using a subset of the japanese simplification corpus. first, in section 3.1, we analyze noise in the japanese simplification corpus and define three representative types of noise that we target. in sections 3.2 to 3.4, we then describe our proposed method for detecting each of the noises. ## experiments to evaluate the effectiveness of the proposed method, we conduct experiments on japanese text simplification. first, we describe the dataset and evaluation metrics in section 4.1, then our experimental setup including models and hyperparameters in section 4.2, and threshold setting on the validation set in section 4.3. finally, section 4.4 presents our experimental results. ## conclusion to improve the performance of japanese text simplification, we proposed methods of parallel corpus filtering to remove noisy sentence pairs from the training dataset in terms of differences in sentence length, synonymy, and fluency. experiments on text simplification models based on transformer and bart showed that parallel corpus filtering based on differences in sentence length and perplexity of language models improved both metrics of bleu and sari over the baseline model without parallel corpus filtering.