Minimum Bayes risk (MBR) decoding is a decision rule of text generation tasks that outperforms conventional maximum a posterior (MAP) decoding using beam search by selecting high-quality outputs based on a utility function rather than those with high-probability. Typically, it finds the most suitable hypothesis from the set of hypotheses under the sampled pseudo-references. mbrs is a library of MBR decoding, which can flexibly combine various metrics, alternative expectation estimations, and algorithmic variants. It is designed with a focus on speed measurement and calling count of code blocks, transparency, reproducibility, and extensibility, which are essential for researchers and developers. We published our mbrs as an MIT-licensed open-source project, and the code is available on GitHub$^{1,2,3}$. - $^1$ GitHub: https://github.com/naist-nlp/mbrs - $^2$ YouTube: https://youtu.be/4qeHpg4PTn0 - $^3$ Demo: https://mbrs-demo.streamlit.app

mbrs: A Library for Minimum Bayes Risk Decoding

Minimum Bayes risk (MBR) decoding achieved state-of-the-art translation performance by using COMET, a neural metric that has a high correlation with human evaluation.
However, MBR decoding requires quadratic time since it computes the expected score between a translation hypothesis and all reference translations.
We propose centroid-based MBR (CBMBR) decoding to improve the speed of MBR decoding.
Our method clusters the reference translations in the feature space, and then calculates the score using the centroids of each cluster.
The experimental results show that our CBMBR not only improved the decoding speed of the expected score calculation 5.7 times, but also outperformed vanilla MBR decoding in translation quality by up to 0.5 COMET in the WMT'22 En$\leftrightarrow$Ja, En$\leftrightarrow$De, En$\leftrightarrow$Zh, and WMT'23 En$\leftrightarrow$Ja translation tasks.

Centroid-Based Efficient Minimum Bayes Risk Decoding

k-nearest-neighbor machine translation (kNN-MT) (Khandelwal et al., 2021) boosts the translation performance of trained neural machine translation (NMT) models by incorporating example-search into the decoding algorithm. However, decoding is seriously time-consuming, i.e., roughly 100 to 1,000 times slower than standard NMT, because neighbor tokens are retrieved from all target tokens of parallel data in each timestep. In this paper, we propose "Subset kNN-MT, which improves the decoding speed of kNN-MT by two methods: (1) retrieving neighbor target tokens from a subset that is the set of neighbor sentences of the input sentence, not from all sentences, and (2) efficient distance computation technique that is suitable for subset neighbor search using a look-up table. Our proposed method achieved a speed-up of up to 132.2 times and an improvement in BLEU score of up to 1.6 compared with kNN-MT in the WMT'19 De-En translation task and the domain adaptation tasks in De-En and En-Ja.

<iframe src="https://app.sli.do/event/532fnPQGAZvefKDC1fZsuL/embed/polls/bfda19ca-cedc-4c6e-91cf-90341ee4e3fc" width="300" height="400"></iframe>

Subset Retrieval Nearest Neighbor Machine Translation

In this paper, we describe our NAIST-NICT-TIT submission to the WMT22 general machine translation task. We participated in this task for the English ↔ Japanese language pair.
Our system is characterized as an ensemble of Transformer big models, k-nearest-neighbor machine translation (kNN-MT) (Khandelwal et al., 2021), and reranking.

In our translation system, we construct the datastore for kNN-MT from back-translated monolingual data and integrate kNN-MT into the ensemble model. We designed a reranking system to select a translation from the n-best translation candidates generated by the translation system. We also use a context-aware model to improve the document-level consistency of the translation.

NAIST-NICT-TIT WMT22 General MT Task Submission

This paper proposes a novel attention mechanism for Transformer Neural Machine Translation, “Synchronous Syntactic Attention,” inspired by synchronous dependency grammars. The mechanism synchronizes source-side and target-side syntactic self-attentions by minimizing the difference between target-side self-attentions and the source-side self-attentions mapped by the encoder-decoder attention matrix. The experiments show that the proposed method improves the translation performance on WMT14 En-De, WMT16 En-Ro, and ASPEC Ja-En (up to +0.38 points in BLEU).

Synchronous Syntactic Attention for Transformer Neural Machine Translation

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W3 (WMT) Seventh Conference on Machine Translation 

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5

SHORT BIO

Presentations

mbrs: A Library for Minimum Bayes Risk Decoding

Centroid-Based Efficient Minimum Bayes Risk Decoding

Subset Retrieval Nearest Neighbor Machine Translation

NAIST-NICT-TIT WMT22 General MT Task Submission

Synchronous Syntactic Attention for Transformer Neural Machine Translation

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