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        <title>Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the Parallel Corpus Filtering task</title>
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    <name type="personal">
        <namePart type="given">Chi-kiu</namePart>
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        <namePart type="given">Michel</namePart>
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    <name type="personal">
        <namePart type="given">Darlene</namePart>
        <namePart type="family">Stewart</namePart>
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    <name type="personal">
        <namePart type="given">Samuel</namePart>
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    <name type="personal">
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    <abstract>We present our semantic textual similarity approach in filtering a noisy web crawled parallel corpus using YiSi—a novel semantic machine translation evaluation metric. The systems mainly based on this supervised approach perform well in the WMT18 Parallel Corpus Filtering shared task (4th place in 100-million-word evaluation, 8th place in 10-million-word evaluation, and 6th place overall, out of 48 submissions). In fact, our best performing system—NRC-yisi-bicov is one of the only four submissions ranked top 10 in both evaluations. Our submitted systems also include some initial filtering steps for scaling down the size of the test corpus and a final redundancy removal step for better semantic and token coverage of the filtered corpus. In this paper, we also describe our unsuccessful attempt in automatically synthesizing a noisy parallel development corpus for tuning the weights to combine different parallelism and fluency features.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6481</identifier>
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        <date>2018-oct</date>
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%0 Conference Proceedings
%T Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the Parallel Corpus Filtering task
%A Lo, Chi-kiu
%A Simard, Michel
%A Stewart, Darlene
%A Larkin, Samuel
%A Goutte, Cyril
%A Littell, Patrick
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 oct
%I Association for Computational Linguistics
%C Belgium, Brussels
%F lo-etal-2018-accurate
%X We present our semantic textual similarity approach in filtering a noisy web crawled parallel corpus using YiSi—a novel semantic machine translation evaluation metric. The systems mainly based on this supervised approach perform well in the WMT18 Parallel Corpus Filtering shared task (4th place in 100-million-word evaluation, 8th place in 10-million-word evaluation, and 6th place overall, out of 48 submissions). In fact, our best performing system—NRC-yisi-bicov is one of the only four submissions ranked top 10 in both evaluations. Our submitted systems also include some initial filtering steps for scaling down the size of the test corpus and a final redundancy removal step for better semantic and token coverage of the filtered corpus. In this paper, we also describe our unsuccessful attempt in automatically synthesizing a noisy parallel development corpus for tuning the weights to combine different parallelism and fluency features.
%R 10.18653/v1/W18-6481
%U https://aclanthology.org/W18-6481
%U https://doi.org/10.18653/v1/W18-6481
%P 908-916

Markdown (Informal)

[Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the Parallel Corpus Filtering task](https://aclanthology.org/W18-6481) (Lo et al., 2018)

• Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the Parallel Corpus Filtering task (Lo et al., 2018)

ACL

• Chi-kiu Lo, Michel Simard, Darlene Stewart, Samuel Larkin, Cyril Goutte, and Patrick Littell. 2018. Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the Parallel Corpus Filtering task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 908–916, Belgium, Brussels. Association for Computational Linguistics.