# Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints

arXiv: https://arxiv.org/abs/2306.12089 · HTML: https://mlsanity.com/paper/2306.12089/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [Guided Open Vocabulary Image Captioning with Constrained Beam Search](https://mlsanity.com/paper/1612.00576/) (2016)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [Lexically Constrained Neural Machine Translation with Levenshtein Transformer](https://mlsanity.com/paper/2004.12681/) (2020)
- [Pointing the Unknown Words](https://mlsanity.com/paper/1603.08148/) (2016)
- [compare-mt: A Tool for Holistic Comparison of Language Generation Systems](https://mlsanity.com/paper/1903.07926/) (2019)
- [Neural Machine Translation Decoding with Terminology Constraints](https://mlsanity.com/paper/1805.03750/) (2018)
- [BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation](https://mlsanity.com/paper/2109.04588/) (2021)
- [Acquiring Knowledge from Pre-trained Model to Neural Machine Translation](https://mlsanity.com/paper/1912.01774/) (2019)
- [EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints](https://mlsanity.com/paper/2011.06868/) (2020)
- [Facilitating Terminology Translation with Target Lemma Annotations](https://mlsanity.com/paper/2101.10035/) (2021)
- [Continuous Learning in Neural Machine Translation using Bilingual Dictionaries](https://mlsanity.com/paper/2102.06558/) (2021)
- [Pointer-based Fusion of Bilingual Lexicons into Neural Machine Translation](https://mlsanity.com/paper/1909.07907/) (2019)
- [Rule-based Morphological Inflection Improves Neural Terminology Translation](https://mlsanity.com/paper/2109.04620/) (2021)
- [Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints](https://mlsanity.com/paper/2204.13355/) (2022)
- [Lingua Custodia's participation at the WMT 2021 Machine Translation using Terminologies shared task](https://mlsanity.com/paper/2111.02120/) (2021)

## Built on by (most cited first, 3 of 3)

- [Adaptable Logical Control for Large Language Models](https://mlsanity.com/paper/2406.13892/) (2024)
- [DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation](https://mlsanity.com/paper/2504.20371/) (2025)
- [Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: A Pilot Study](https://mlsanity.com/paper/2504.16601/) (2025)

## Abstract (arXiv)

Lexically-constrained NMT (LNMT) aims to incorporate user-provided terminology into translations. Despite its practical advantages, existing work has not evaluated LNMT models under challenging real-world conditions. In this paper, we focus on two important but under-studied issues that lie in the current evaluation process of LNMT studies. The model needs to cope with challenging lexical constraints that are "homographs" or "unseen" during training. To this end, we first design a homograph disambiguation module to differentiate the meanings of homographs. Moreover, we propose PLUMCOT, which integrates contextually rich information about unseen lexical constraints from pre-trained language models and strengthens a copy mechanism of the pointer network via direct supervision of a copying score. We also release HOLLY, an evaluation benchmark for assessing the ability of a model to cope with "homographic" and "unseen" lexical constraints. Experiments on HOLLY and the previous test setup show the effectiveness of our method. The effects of PLUMCOT are shown to be remarkable in "unseen" constraints. Our dataset is available at https://github.com/papago-lab/HOLLY-benchmark