Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity (2022)

Abstract: This paper introduces our neural machine translation system’s participation in the WAT 2022 shared translation task (team ID: sakura). We participated in the Parallel Data Filtering Task. Our approach based on Feature Decay Algorithms achieved +1.4 and +2.4 BLEU points for English to Japanese and Japanese to English respectively compared to the model trained on the full dataset, showing the effectiveness of FDA on in-domain data selection.

## introduction this paper introduces our neural machine translation (nmt) systems' participation in the 9th workshop on asian translation (wat-2022) shared translation tasks @xcite . we participated in the parallel corpus filtering task 1 and our team id is sakura. the task consists of domain specific data selection out of noisy parallel corpus mined from the web. the goal is to build @xmath0. the constraint is to select the data from jparacrawl v3.0 @xcite . only a subset of the data should be extracted and no other actions, such as transformation or augmentation, are allowed. the models built with this data are evaluated using the test set from as-pec @xcite , a scientific domain parallel corpus. in our submissions, we used two independent techniques viz. feature decay algorithms @xcite @xcite (fda) and log-likelihood scores. fda based submission achieved +1.4 and +2.4 bleu for @xmath1. log-likelihood based submission achieved +0.5 bleu for @xmath2. ## data selection in this section we detail our approach to select domain specific sentences. our approach aimed to extract the sentences from jparacrawl @xcite ) that were in-domain, based on the train set of aspec @xcite . ## model we trained both @xmath3. we follow the details from the organizers 3 and build transformer @xcite models using fairseq @xcite ## results in figure 3 and 4 shows the detailed breakdown of adequacy scores from the organisers. no other team participated in the task, so top system summary is detailed in the ## conclusion we presented our submissions (team id: sakura) to the wat 2022 parallel data filtering task in this paper. we described our data selection system based on fda and log-probability scores. fda based filtering showed effectiveness in finding a subset of parallel sentences that were more useful to train a scientific-domain nmt model than using all the sentences. our system was trained just on a 20% of the data and achieved +1.4 bleu improvement over the baseline in the @xmath6.4 for @xmath7. as a future work, we want to use fda in combination with the normalised log-probability scores. the work of @xcite demonstrated that the inclusion metrics such as lexical richness can boost the performance of fda. more generally, we plan to explore how can we improve a scientific domain nmt model, by using limited amount of aspec data along with jparacrawl. the motivation is to gauge the efficacy of fda based approach in data selection where very less in-domain data is available along with lot of noisy mixed domain data.