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        <title>SNAPP: Segment-Level Neural Alignment Post-Processing for Low-Resource Parallel Sentence Mining</title>
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    <abstract>Machine Translation (MT) for low-resource languages has been challenging so far, mainly due to the small size or lack of parallel sentences. Parallel sentence mining and filtering are two automatic approaches which aim to identify and curate such resources. Yet, they usually rely on multilingual representation as a backend, which is known to be of poorer quality for low-resource language pairs, leading to the introduction of noisy pairs. We devise SNAPP, a post-processing pipeline which combines a segment-level filtering technique with an unsupervised neural aligner to remove highly similar but non-parallel sentence pairs. Modular components further enable more specific adaptation to the language pair under consideration. We evaluate filtering performance on three language pairs of varying typological distance. Finally, we show improvement on the downstream MT performance with our post-processing pipeline for the two harder language pairs.</abstract>
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%0 Conference Proceedings
%T SNAPP: Segment-Level Neural Alignment Post-Processing for Low-Resource Parallel Sentence Mining
%A Hettich, Niklas
%A Okabe, Shu
%A Fraser, Alexander
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F hettich-etal-2026-snapp
%X Machine Translation (MT) for low-resource languages has been challenging so far, mainly due to the small size or lack of parallel sentences. Parallel sentence mining and filtering are two automatic approaches which aim to identify and curate such resources. Yet, they usually rely on multilingual representation as a backend, which is known to be of poorer quality for low-resource language pairs, leading to the introduction of noisy pairs. We devise SNAPP, a post-processing pipeline which combines a segment-level filtering technique with an unsupervised neural aligner to remove highly similar but non-parallel sentence pairs. Modular components further enable more specific adaptation to the language pair under consideration. We evaluate filtering performance on three language pairs of varying typological distance. Finally, we show improvement on the downstream MT performance with our post-processing pipeline for the two harder language pairs.
%U https://aclanthology.org/2026.wmt-main.14/
%P 239-253

Markdown (Informal)

[SNAPP: Segment-Level Neural Alignment Post-Processing for Low-Resource Parallel Sentence Mining](https://aclanthology.org/2026.wmt-main.14/) (Hettich et al., WMT 2026)

• SNAPP: Segment-Level Neural Alignment Post-Processing for Low-Resource Parallel Sentence Mining (Hettich et al., WMT 2026)

ACL

• Niklas Hettich, Shu Okabe, and Alexander Fraser. 2026. SNAPP: Segment-Level Neural Alignment Post-Processing for Low-Resource Parallel Sentence Mining. In Proceedings of the Eleventh Conference on Machine Translation, pages 239–253, Budapest, Hungary. Association for Computational Linguistics.