Transn’s Submissions to the WMT 22 Translation Suggestion Task (2022)

Abstract: This paper describes the Transn’s submissions to the WMT2022 shared task on TranslationSuggestion. Our team participated on two tasks: Naive Translation Suggestion and TranslationSuggestion with Hints, focusing on two language directions Zh→En and En→Zh. Apart from the golden training data provided by the shared task, we utilized synthetic corpus to fine-tune on DeltaLM (∆LM), which is a pre-trained encoder-decoder language model. We applied two-stage training strategy on ∆LM and several effective methods to generate synthetic corpus, which contribute a lot to the results. According to the official evaluation results in terms of BLEU scores, our submissions in Naive Translation Suggestion En→Zh and Translation Suggestion with Hints (both Zh→En and En→Zh) ranked 1st, and Naive Translation Suggestion Zh→En also achieved comparable result to the best score.

## introduction combining machine translation (mt) and human translation (ht) is becoming a popular way in translation practice, which uses a typical way of post edit (pe) -the human translators are asked to provide alternatives for the incorrect word spans in the results generated by mt @xcite @xcite @xcite . in order to improve the efficiency of pe, researchers proposed translation suggestion (ts) to provide the sub-segment suggestions for the annotated incorrect word spans, and experiments show that ts can substantially reduce translators' cognitive loads and the postediting time @xcite @xcite . this paper describes the contribution of transn iol research to the wmt22 translation suggestion shared task, where systems were submitted to two tasks: 1) naive translation suggestion; 2) translation suggestion with hints. for both tasks we trained the models on pre-trained encoderdecoder language model ∆lm @xcite with the corpus which were synthesized deliberately, then submitted the ensemble results of the trained models. our main contributions are: the rest of this paper is organized as below. section 2 is a brief description for translation suggestion shared task of wmt2022. section 3 presents our system, including data constructing and the training process with ∆lm. section 4 reports experimental results in the participated language directions. finally, we conclude our work in section 5. ## translation suggestion tasks translation suggestion is a new task on wmt2022, which includes two sub-tasks. task 1 -naive translation suggestion: this sub-task focuses on the scenario where the user selects the incorrect span of the mt sentence without entering any information, the model outputs the alternatives automatically. consider the source sentence x, the mt sentence m, the incorrect span selected by the user as w, the alternative y and the model parameter θ, the naive ts can be formulated as: p (y|x, m, w, θ). task2 -translation suggestion with hints: in actual applications, users usually have general ideas of what they want. if they are dissatisfied with all the suggestions provided by naive ts, they are willing to enter some hints for the model to generate more accurate suggestions. given the hints h provided by users, the sub-task 2 can be formulated as: p (y|x, m, w, h, θ). in this task, we take the topk initial characters of the alternative words as the hint, and the k is randomly selected for each example. task 1 includes 4 language directions (en⇔zh and en⇔de) and task 2 includes 2 language directions (en⇔zh). we participated en⇔zh language directions for both tasks. ## implemented systems we fine-tune the pre-trained language model ∆lm on synthetic data and the task golden data for task1, then adjust n-best parameter along with an optimization algorithm for task2. the details are described in this section. ## experiments we present the performance of the implemented models on the dev and test datasets, as well as some additional analysis. ## conclusions we present the transn iol research submissions of the wmt2022 shared task on translation suggestion. our system is implemented with twostage fine-tuning on ∆lm, which is a pre-trained encoder-decoder language model. to improve the performance, we construct synthetic data by difference comparison, named-entity masking and random sampling on parallel corpus. we propose an effective algorithm to choose optimal translation suggestion with hints. the accuracy indicator of ts is also analyzed for more efficient pe in practice. on the participated 4 tracks of en⇔zh language directions, we achieved best scores on 3 tracks and comparable result on another track. effective translation suggestions benefit a lot for post editing. in the future, we plan to research field related and fine-grained ts models to improve system performance, and will integrate these advanced techniques in our translation practice.