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<!doctype html>
<html>
<head>
<meta charset="utf-8" />
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
<!-- Replace the content tag with appropriate information -->
<meta
name="description"
content="mbrs: A library for minimum Bayes risk decoding"
/>
<meta property="og:title" content="mbrs" />
<meta
property="og:description"
content="brs: A library for minimum Bayes risk decoding"
/>
<meta property="og:url" content="https://naist-nlp.github.io/mbrs-web/" />
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<meta property="og:image:width" content="1200" />
<meta property="og:image:height" content="630" />
<meta name="twitter:title" content="mbrs" />
<meta
name="twitter:description"
content="A library for minimum Bayes risk decoding"
/>
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<!-- Keywords for your paper to be indexed by-->
<meta
name="keywords"
content="mbrs: A library for minimum Bayes risk decoding"
/>
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<title>mbrs</title>
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<script>
hljs.initHighlightingOnLoad();
</script>
</head>
<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">
mbrs: A library for minimum Bayes risk decoding
</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a
href="https://sites.google.com/view/hdeguchi"
target="_blank"
>Hiroyuki Deguchi</a
>,</span
>
<span class="author-block">
<a href="https://www.yusuke1997.jp/" target="_blank"
>Yusuke Sakai</a
>,</span
>
<span class="author-block">
<a
href="https://sites.google.com/site/hidetakakamigaito/"
target="_blank"
>Hidetaka Kamigaito</a
>,</span
>
<span class="author-block">
<a
href="https://sites.google.com/site/tarowtnb/"
target="_blank"
>Taro Watanebe</a
>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"
>Nara Institute of Science and Technology (NAIST)
</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a
href="https://arxiv.org/pdf/2408.04167"
target="_blank"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a
href="https://github.com/naist-nlp/mbrs"
target="_blank"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- Readthedocs link -->
<span class="link-block">
<a
href="https://mbrs.readthedocs.io/en/latest/"
target="_blank"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="cib-read-the-docs"></i>
</span>
<span>Docs</span>
</a>
</span>
<!-- ArXiv abstract Link -->
<span class="link-block">
<a
href="https://arxiv.org/abs/2408.04167"
target="_blank"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Demonstration link -->
<span class="link-block">
<a
href="https://mbrs-demo.streamlit.app/"
target="_blank"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<img src="static/svgs/streamlit-mark-light.svg" />
</span>
<span>Demo</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Icon -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<div class="item">
<!-- Your image here -->
<img
src="static/svgs/icon.svg"
alt="mbrs"
width="300px"
style="display: block; margin: 0 auto"
/>
</div>
</div>
</div>
</section>
<!-- End Icon -->
<!-- Paper abstract -->
<section class="hero is-small">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Minimum Bayes risk (MBR) decoding is a decision rule of
generation tasks that outperforms conventional maximum a
posterior (MAP) decoding using beam search by selecting
high-quality outputs rather than high-probability. Typically, it
finds the most probable hypothesis from the set of hypotheses
under the sampled pseudo-references. mbrs is a library of MBR
decoding, which can flexibly combine various metrics,
expectation estimations, and algorithmic variants. It is
designed with a focus on scope-based speed measurement and
calling count, 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
<a href="https://github.com/naist-nlp/mbrs">GitHub</a>.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@misc{deguchi-2024-mbrs,
title={mbrs: A Library for Minimum Bayes Risk Decoding},
author={Hiroyuki Deguchi and Yusuke Sakai and Hidetaka Kamigaito and Taro Watanabe},
year={2024},
eprint={2408.04167},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.04167},
}
</code></pre>
</div>
</section>
<!--End BibTex citation -->
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