T erm M ind: A libaba’s WMT 21 Machine Translation Using Terminologies Task Submission (2021)

Abstract: This paper describes our work in the WMT 2021 Machine Translation using Terminologies Shared Task. We participate in the shared translation terminologies task in English to Chinese language pair. To satisfy terminology constraints on translation, we use a terminology data augmentation strategy based on Transformer model. We used tags to mark and add the term translations into the matched sentences. We created synthetic terms using phrase tables extracted from bilingual corpus to increase the proportion of term translations in training data. Detailed pre-processing and filtering on data, in-domain finetuning and ensemble method are used in our system. Our submission obtains competitive results in the terminology-targeted evaluation.

## introduction terminology is important for domain-specific machine translation. each domain has its own terminology, which represents the important and core concepts in the domain. in the workflow of human translation, terminology is an effective method to integrate the knowledge of human translator into machine translations @xcite @xcite . one line of approach is "hard constraint". the terminology is ensured to appear in the translation by adding constraints in beam search decoding @xcite . however, the enforcement of terminology constraints tends to reduce the fluency of translation @xcite , especially when there are multiple constraints or the constraint is noisy @xcite . another line of approach is "soft constraint". training data is augmented with placeholders or additional terminology translations @xcite @xcite @xcite . the above methods assume that the terminology translations are good ones. however, in industry or real world the terminology translations may be noisy @xcite . and in the human translation workflows the terminology constraints usually need to be applied hierarchically according to priority. in these scenarios one source term will have more than one translation. therefore, we are happy to participate in this task and develop the method to deal with 1-to-many term translations in neural machine translation systems. the structure of the paper is as follows. section 2 describes the dataset, data pre-processing and selection. we introduce details of our system in section 3. the experiment settings, terminologies used in training and main results are introduced in section 4. finally, we conclude our work in section 5. ## system overview in this section, we will describe the details about the model and techniques of our work. first, we will introduce the terminology data augmentation strategy to improve terminology translation accuracy. then, different transformer model architectures we adopted in the paper will be depicted. finally, we will introduce several strategies to train our models for performance improvement. ## conclusion this paper presents the submissions by alibaba for wmt 2021 english to chinese translation terminologies task. we have applied a terminology data augmentation method to integrate term translations into nmt systems. we also used a series of data filtering strategies, fine-tuning and ensemble methods to improve the system performance. experimental results show the method can improve terminologies translation performance.