VARCO - MT : NCSOFT ’s WMT ’23 Terminology Shared Task Submission (2023)

Abstract: A lack of consistency in terminology translation undermines quality of translation from even the best performing neural machine translation (NMT) models, especially in narrow domains like literature, medicine, and video game jargon. Dictionaries containing terminologies and their translations are often used to improve consistency but are difficult to construct and incorporate. We accompany our submissions to the WMT ‘23 Terminology Shared Task with a description of our experimental setup and procedure where we propose a framework of terminology-aware machine translation. Our framework comprises of an automatic terminology extraction process that constructs terminology-aware machine translation data in low-supervision settings and two model architectures with terminology constraints. Our models outperform baseline models by 21.51%p and 19.36%p in terminology recall respectively on the Chinese to English WMT’23 Terminology Shared Task test data.

## introduction the wmt'23 terminology shared task aims to assess machine translation models' abilities to leverage additional information. a terminology dictionary is provided with each line of source text. this is particularly useful for terminology consistency. the wmt'23 terminology shared task includes the language pairs chinese-english, english-czech, and german-english. we focus our submission on the chinese-english pair. the task consists of three modes, as shown in mode 1 assesses translation quality without additional terminology information. mode 2 assesses translation quality with additional terminology information. mode 3 assesses translation quality with a glossary containing random non-terminology. in this paper, we describe our model building process for terminology translation from data pre-processing to model evaluation. we present two transformer-based encoder-decoder models: terminology self-selection neural machine translation (tssnmt) and forcegen transformer (forcegen-t). tssnmt uses a shared encoder with a gating mechanism @xcite , allowing the model to determine the weights of the source sentence and terminologies to use during generation. forcegen-t enforces a decoder to generate the terminologies via force decoding @xcite and copy mechanism @xcite , which enables the model to attend to terminologies during generation. both models significantly outperform the baseline model. first, a data-driven approach where terminologies are appended to input sentences @xcite . @xcite suggest using copy mechanism to instruct the model to replicate the target terminology during the generation process. second, an alternative approach focuses on manipulating the model architecture. @xcite have used input sentences and their corresponding retrieved translation pairs to encode conditional source target memory. this approach uses a gated multi-source attention mechanism, which takes the encoded representation and the hidden state of the source as input, thereby steering the model toward the generation of the intended translation. third, efforts have been directed at tailoring the decoding process to incorporate terminologies. @xcite and @xcite have introduced constrained decoding techniques that reinforce the translated output's pre-specified terminologies. ## data process the wmt'23 terminology shared task is a constrained track, following the same rules of data usage as the wmt'23 general mt task, forbidding the use of external data. however, unlike the wmt'21 terminology task, the provided training data lacks terminology information, and need to be artificially constructed. ## models this section presents two distinct models designed to incorporate terminologies into nmt models. the first model, terminology self-selection neural machine translation (tssnmt), employs a shared encoder architecture featuring a gating mechanism. this mechanism empowers the model to make decisions regarding the proportion of the source sentence and the terminologies to be processed during generation. the second model, forcegen transformer (forcegen-t), takes a more straightforward approach, utilizing a standard transformer model with force decoding @xcite and copy mechanism @xcite . this approach enforces the model to initially generate the predefined terminologies before generating the remainder of the sentence. copy mechanism is applied to replicate source-side target terminologies in the output. we have implemented the tssnmt model with minor changes to the transformer architecture. the model has two encoders, as shown in figure 2 . each encoder receives input in source sentences and source-target pair terminologies. these two encoders share parameters and encode both the source sentences and the terminologies. the decoder calculates cross-attentions with these two encoder hidden states separately and then projects them to a gating mechanism @xcite . sharing parameters allows the model to decide weight distribution across the source and the terminology during generation. ## results ## conclusion this paper presents the comprehensive procedure of our submissions for the wmt'23 terminology shared task. our approach involves meticulous refinement and pre-processing of the provided data, subsequently used to train our models. we investigate and implement two strategies for effectively integrating the given terminologies into the output, demonstrating their superior performance compared to the baseline. the result shows that our approach can significantly improve translation accuracy by increasing the recall of terminologies. as a future endeavor, we aim to extend the validation of our approach to other languages.