Disambiguated Lexically Constrained Neural Machine Translation (2023)

Abstract: Lexically constrained neural machine translation (LCNMT), which controls the translation generation with pre-specified constraints, is important in many practical applications. Current approaches to LCNMT typically assume that the pre-specified lexicon constraints are contextually appropriate. This assumption limits their application to real-world scenarios where a source lexicon may have multiple target constraints, and disambiguation is needed to select the most suitable one. In this paper, we propose disambiguated LCNMT (D-LCNMT) to solve the problem. D-LCNMT is a robust and effective two-stage framework that disambiguates the constraints based on contexts at first, then integrates the disambiguated constraints into LCNMT. Experimental results show that our approach outperforms strong baselines including existing data argumentation based approaches on benchmark datasets, and comprehensive experiments in scenarios where a source lexicon corresponds to multiple target constraints demonstrate the constraint disambiguation superiority of our approach.

## introduction lexically constrained neural machine translation (lcnmt) is a task that guarantees the inclusion of specific lexicons in the translation, which is of great importance in many applications such as interactive translation with user-given lexicon constraints @xcite , domain adaptation with pre-specified terminology constraints @xcite . accurate lexicon translation plays a key role in improving translation quality. however, in real world applications, a source lexicon often has multiple translation constraints, which are provided by a specific database and represent different but core concepts. it is essential for a translation model to select the most contextually appropriate constraint and force it to appear in the trans-figure 1 : an example of the constraint ambiguity problem in english-to-chinese translation. given a lexical constraint inventory, the lexicon airway has three possible translations as the ambiguous constraints: respiratory tract, airline, and ventiduct, among which respiratory tract is the context appropriate one for the input sentence. major works in this task apply data augmentation approach, which builds synthetic corpora containing ambiguous constraints via code-switching, and train the nmt models to select the most contextually appropriate constraint implicitly @xcite . instead, our d-lcnmt adopts an explicit twostage framework that performs constraint disambiguation and integration into nmt sequentially, and outperforms the above data augmentation approach on benchmark datasets. in particular, at the first stage, we build a constraint disambiguation network based on contrastive learning so that the correct constraint is selected given the source lexicon and its context in the given source sentence. at the second stage, we integrate the most appropriate constraint obtained in the first stage into nmt with the help of current lexically constrained approaches @xcite . experiments on disambiguated lexically constrained translation tasks in german-to-english and englishto-chinese show that our approach significantly outperforms strong baselines including the data augmentation approach. for lexicons that have multiple possible constraints, our approach achieves state-of-the-art accuracy of constraint disambiguation, especially ranks the first in the leaderboard of wmt2021 shared task on machine translation using terminologies. overall, our contributions are three-fold: ## related work we introduce lcnmt at first, then introduce the related constraint disambiguation researches. ## d-lcnmt we propose d-lcnmt to solve the ambiguous constraint problem for lcnmt through a twostage framework. at stage 1, we introduce a contrastive learning based constraint disambiguation neural network. at stage 2, we integrate the disambiguated constraints into current competitive lcnmt models @xcite . ## experiments we conduct experiments on german-to-english (de-en) and english-to-chinese (en-zh) lexically constrained translation tasks. different to major works on lcnmt that only use gold constraints, our experiment focuses on more practical scenario that ambiguous constraints exist given the input source sentences. ## conclusion in this paper, we propose an effective two-stage framework for disambiguated lexically constrained neural machine translation (d-lcnmt). our basic idea is to build a continuous representation space for constraint disambiguation at stage 1, then inject the disambiguated constraints into the vectorized or template-based lcnmt models at stage 2. experiments show that our approach is significantly better than various representative systems across de-en and en-zh translations, showing significant superiority in constraint disambiguation, which is wide spread and important in lexically constrained machine translation.