The ADAPT System Description for the STAPLE 2020 E nglish-to- P ortuguese Translation Task (2020)

Abstract: This paper describes the ADAPT Centre’s submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task. In this shared task, the participants were asked to produce high-coverage sets of plausible translations given English prompts (input source sentences). We present our English-to-Portuguese machine translation (MT) models that were built applying various strategies, e.g. data and sentence selection, monolingual MT for generating alternative translations, and combining multiple n-best translations. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Portuguese translation task.

## introduction the adapt centre participated in staple 1 @xcite , a shared task of the 4th wngt which will be held at acl 2020, 2 in the english-to-portuguese language direction. the task focuses on a specific use case of mt, i.e. generating many possible translations for a given input text. such situations are usually seen on language-learning platforms (e.g. duolingo) where the learning process includes translation-based exercises, and evaluation is done by comparing learners' responses with a large set of human-curated acceptable translations. the shared task organisers (duolingo) have released real language-learner data of duolingo as training examples. we applied a number of strategies to our mt system building process, e.g. monolingual mt, extracting parallel sentences that are similar to the duolingo's 1 https://sharedtask.duolingo.com/ 2 https://acl2020.org/ real language-learner data from the freely available external parallel corpora, assembling n-best translations from multiple translation systems, which essentially led us to generate high-coverage sets of possible translations of the english prompts (input source sentences). the remainder of the paper is organised as follows: section 2 explains our approaches; section 3 details of the datasets used, explains the experimental setups and presents the results with some discussions; and section 4 concludes our work with avenues for future work. ## conclusion this paper presents the adapt translation system for the staple 2020 english-to-portuguese translation task. we aimed to build a competitive translation system that can produce high-coverage sets of plausible translations given english prompts (input source sentences). for this, we applied various strategies, e.g. selecting data sources that are similar to staple 2020 training data, selecting sentences from external corpora, applying monolingual mt for generating alternative translations, combining translations produced by multiple mt systems. we found that the systematic addition of these techniques to baseline yields moderate improvement over the baseline (0.0109 f 1 point absolute corresponding to 2.4% relative gain). the best experimental setup earned us the fifth position in the competition. in the future, we aim to apply confusion network decoding in order to re-rank n-best translations generated by the multiple mt systems. we used monolingual mt for generating alternative sentences for the target (portuguese) translations. we also aim to apply this strategy to the english prompts (i.e. source-side of the translation-pair) for generating alternative sequences of the input source sentences.