---
title: "Incorporating Discrete Translation Lexicons — Arthur 2016"
description: "Attentional NMT systems often get rare content words wrong."
authors: ["Philip Arthur", "Graham Neubig", "Satoshi Nakamura"]
year: 2016
venue: "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D16-1162/"
doi: "10.18653/v1/d16-1162"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/arthur-2016-incorporating-discrete-translation-lexicons-into/"
---
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# Incorporating Discrete Translation Lexicons into Neural Machine Translation

**Authors:** Philip Arthur, [Graham Neubig](https://md.research.elamur.ai/free/en/authors/graham-neubig/), [Satoshi Nakamura](https://md.research.elamur.ai/free/en/authors/satoshi-nakamura/) · **Year:** 2016 · **Venue:** Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D16-1162/) · [PDF](https://aclanthology.org/D16-1162.pdf) · [DOI 10.18653/v1/d16-1162](https://doi.org/10.18653/v1/d16-1162) · [OpenAlex](https://openalex.org/W2962708992)

**Citations:** 203 citations (openalex-snapshot · 2026-10-14) · [95](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Attentional NMT systems often get rare content words wrong. They substitute a word that fits the context but changes the meaning, for example outputting "Norway" when the source says "Tunisia". Phrase-based systems rarely make this error because they rely on explicit word mappings. Arthur, Neubig and Nakamura add discrete probabilistic translation lexicons to an attentional encoder-decoder in the style of Luong et al. (2015a).

For each source sentence, they build a matrix of word-level lexical probabilities. At every decoding step, the current attention vector weights its columns, producing a lexicon-derived distribution over the next target word. This distribution is merged with the NMT output in one of two ways:
- as a log-space bias added before the softmax, or
- through linear interpolation with a learnable coefficient.

The lexicons come from IBM-model alignments of the training data, a handmade dictionary, or a hybrid of both. On two English-Japanese corpora, the approach raises BLEU by 2.0–2.3 and NIST by 0.13–0.44, and training converges faster. The work shows a cheap way to bring the reliability of symbolic resources into neural translation.

## Key points
- **Combination methods:** The bias variant adds log(p_l + ϵ) to the softmax input, with ϵ = 0.001. The linear variant uses λ = sigmoid(x), with x learned and initialized so that λ = 0.5.
- **Lexicon sources and results:** Three lexicon types were tested: auto (alignment-based), man (handmade) and hyb (hybrid). The bias method helped auto and hyb but not man. The linear method helped only man, whose source-vocabulary coverage was 35.3% on BTEC and 9.7% on KFTT. Linear interpolation was generally weaker than the bias method.
- **Attention sharpness:** The auto-bias model's attention was more focused than the baseline's, with per-word entropy of 3.23 bits versus 3.81 bits for the attn baseline.
- **Scaling test:** On the larger ASPEC corpus (2M sentence pairs, 63M tokens, 81K vocabulary), BLEU rose from 20.82 (attn) to 22.66 (auto-bias).
- **Relation to prior work:** Copy mechanisms and the vocabulary restriction of Mi et al. (2016) are framed as special cases of this approach. The authors list context-dependent λ as future work.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Cited by

Cited by

95 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 88 not yet.

- [A Survey of Domain Adaptation for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/chu-2018-survey-domain-adaptation-neural-machine/)
- [Stronger Baselines for Trustable Results in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/denkowski-2017-stronger-baselines-trustable-results-neural/)
- [Jointly Learning to Align and Translate with Transformer Models](https://md.research.elamur.ai/free/en/papers/garg-2019-jointly-learning-align-translate-transformer/)
- [Domain Robustness in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/muller-2020-domain-robustness-neural-machine-translation/)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/post-2018-fast-lexically-constrained-decoding-dynamic/)
- [An Analysis of Encoder Representations in Transformer-Based Machine Translation](https://md.research.elamur.ai/free/en/papers/raganato-2018-analysis-encoder-representations-transformer-based/)
- [Benchmarking Machine Translation with Cultural Awareness](https://md.research.elamur.ai/free/en/papers/yao-2023-benchmarking-machine-translation-cultural-awareness/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{arthur2016incorporating,
  title = {{Incorporating Discrete Translation Lexicons into Neural Machine Translation}},
  author = {Arthur, Philip and Neubig, Graham and Nakamura, Satoshi},
  booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
  year = {2016},
  doi = {10.18653/v1/d16-1162},
  url = {https://doi.org/10.18653/v1/d16-1162}
}
```

- [← previous Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://md.research.elamur.ai/free/en/papers/gu-2016-incorporating-copying-mechanism-sequence-sequence/)
- [next → Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora](https://md.research.elamur.ai/free/en/papers/hamilton-2016-inducing-domain-specific-sentiment-lexicons/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/page/2/)

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