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 lexical 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 augmentation
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.

本文介绍了一种基于上下文的约束消岐技术，提出了一种称为 D-LCNMT 的框架，使得预先指定的词汇约束在真实世界的情境中更加合适和精确，实验证明该方法在数据扩充和多个目标约束词汇方面优于先前的方法。

词汇消歧约束的神经机器翻译

Disambiguated Lexically Constrained Neural Machine Translation

Lexically constrained neural machine translation (NMT), which controls the
generation of NMT models with pre-specified constraints, is important in many
practical scenarios. Due to the representation gap between discrete constraints
and continuous vectors in NMT models, most existing works choose to construct
synthetic data or modify the decoding algorithm to impose lexical constraints,
treating the NMT model as a black box. In this work, we propose to open this
black box by directly integrating the constraints into NMT models.
Specifically, we vectorize source and target constraints into continuous keys
and values, which can be utilized by the attention modules of NMT models. The
proposed integration method is based on the assumption that the correspondence
between keys and values in attention modules is naturally suitable for modeling
constraint pairs. Experimental results show that our method consistently
outperforms several representative baselines on four language pairs,
demonstrating the superiority of integrating vectorized lexical constraints.

该研究提出了一种直接将词汇约束集成到神经机器翻译模型中的方法，通过将词汇约束向量化为连续的键和值，并利用 NMT 模型的注意力模块来表征词汇约束对，并在四种语言对上表明了该方法的优越性。