Character Mapping and Ad-hoc Adaptation: E dinburgh’s IWSLT 2020 Open Domain Translation System (2020)

Abstract: This paper describes the University of Edinburgh’s neural machine translation systems submitted to the IWSLT 2020 open domain Japanese \leftrightarrow Chinese translation task. On top of commonplace techniques like tokenisation and corpus cleaning, we explore character mapping and unsupervised decoding-time adaptation. Our techniques focus on leveraging the provided data, and we show the positive impact of each technique through the gradual improvement of BLEU.

## introduction the university of edinburgh presents its neural machine translation (nmt) systems for the iwslt 2020 open domain translation task @xcite . the task requires participants to submit systems to translate between japanese (ja) and chinese (zh), where the sentences come from mixed domains. for training purpose, 1.96 million existing sentence pairs and 59.49 million crawled sentence pairs 1 are provided, making the task a high-resource one. in our experiments, we focused on three aspects: our techniques are mostly data-centric and each technique improves translation in terms of bleu on our development set. in the final automatic evaluation based on 4-gram character bleu, our systems rank 6 th out of 14 for @xmath0. ## chinese and japanese mapping in ancient times, japanese borrowed (at that time, traditional) chinese characters (hanzi) to use as a written form (kanji). after a long time of coand separate evolution (e.g. chinese simplification), the relationship between hanzi and kanji is complicated. some hanzi and kanji stay unchanged, some develop different meanings, and some develop different written forms. a detailed description is given by @xcite . more importantly, they released a kanji to traditional and simplified hanzi mapping table. with each kanji being a key, there can be zero, one or many corresponding traditional and simplified hanzi. in total, there are mapping entries for around 5700 kanji to simplified hanzi. @xcite use this character mapping to enhance word segmentation in statistical machine translation (smt). recently, @xcite map characters in a chinese corpus to japanese, making it a pseudo-japanese corpus for the purpose of pre-training japanese↔english nmt. in our work, we take a step forward to map chinese and japanese to each other for chinese↔japanese nmt directly. without mapping as a data processing step, an nmt system needs to learn the mapping between kanji and hanzi implicitly. therefore we hypothesise that mapping them before training a model will: since we already simplified all chinese characters, hereafter we refer to simplified chinese as hanzi. mapping from kanji to hanzi is straightforward from the character mapping relationship between hanzi and kanji. in order to simplify post-processing. we only map source characters to target, so the target outputs are always in the genuine target language. hence we map chinese to japanese or japanese to chinese depending on the translation direction. we design two simple mapping scheme variants: all target characters must be constrained to target corpus, in order not to introduce new characters. we then train models on the mapped data for both directions, with results displayed in ## filtering based on cross-entropy our initial rule-based cleaning shows its effectiveness through improvement in bleu scores. we further adopt two filtering steps based on crossentropy proposed by junczys-dowmunt (2018): ## ad-hoc domain adaptation nmt is sensitive to domain mismatch @xcite , and there are numerous techniques for domain adaptation for nmt @xcite . some model and training techniques require prior knowledge of the domain and cannot be easily applied. nonetheless, one method that can be adopted during test sentence translation is retrieving samples that are similar to the input from the available training data, and fine-tuning a trained generic model on these samples. such ad-hoc domain adaptation can be done at sentence level @xcite or document level @xcite . ## results and conclusion in our work, we explore a series of techniques which lead to improvements on ja↔zh nmt. rule-based filtering brings a marginal increment in bleu for @xmath3. character mapping, which increases source and target vocabulary overlap, has a tiny effect on @xmath4, but makes 1 bleu improvement for @xmath5. next, cross-entropy filtering adds 2.5 bleu for @xmath6. ad-hoc fine-tuning, aiming at enhancing open domain translation, delivers another 0.55 bleu. finally, an ensemble of 4 finetuned models boosts up 1 bleu. overall, our work has improved 10 and more than 3 bleu for @xmath7. character mapping between japanese and chinese may inspire two directions of research: applying character mapping on other tasks, and trying character mapping for other language pairs. due to time constraint, we could not perform exhaustive experiments to find the best configuration for sentence-level and cluster-level adaptation, which can be further investigated. we also propose to study further on cluster (sub-document) adaptation, where a system can group test sentences, and fine-tune before translating them. this can make adaptation more fine-grained compared to document adaptation, without the huge risk of overfitting at sentence-level.