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    <titleInfo>
        <title>Composing Byte-Pair Encodings for Morphological Sequence Classification</title>
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        <namePart type="given">Adam</namePart>
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            <title>Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)</title>
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        <name type="personal">
            <namePart type="given">Marie-Catherine</namePart>
            <namePart type="family">de Marneffe</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Miryam</namePart>
            <namePart type="family">de Lhoneux</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Joakim</namePart>
            <namePart type="family">Nivre</namePart>
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    <abstract>Byte-pair encodings is a method for splitting a word into sub-word tokens, a language model then assigns contextual representations separately to each of these tokens. In this paper, we evaluate four different methods of composing such sub-word representations into word representations. We evaluate the methods on morphological sequence classification, the task of predicting grammatical features of a word. Our experiments reveal that using an RNN to compute word representations is consistently more effective than the other methods tested across a sample of eight languages with different typology and varying numbers of byte-pair tokens per word.</abstract>
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%0 Conference Proceedings
%T Composing Byte-Pair Encodings for Morphological Sequence Classification
%A Ek, Adam
%A Bernardy, Jean-Philippe
%Y de Marneffe, Marie-Catherine
%Y de Lhoneux, Miryam
%Y Nivre, Joakim
%Y Schuster, Sebastian
%S Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F ek-bernardy-2020-composing
%X Byte-pair encodings is a method for splitting a word into sub-word tokens, a language model then assigns contextual representations separately to each of these tokens. In this paper, we evaluate four different methods of composing such sub-word representations into word representations. We evaluate the methods on morphological sequence classification, the task of predicting grammatical features of a word. Our experiments reveal that using an RNN to compute word representations is consistently more effective than the other methods tested across a sample of eight languages with different typology and varying numbers of byte-pair tokens per word.
%U https://aclanthology.org/2020.udw-1.9
%P 76-86

Markdown (Informal)

[Composing Byte-Pair Encodings for Morphological Sequence Classification](https://aclanthology.org/2020.udw-1.9) (Ek & Bernardy, UDW 2020)

• Composing Byte-Pair Encodings for Morphological Sequence Classification (Ek & Bernardy, UDW 2020)

ACL

• Adam Ek and Jean-Philippe Bernardy. 2020. Composing Byte-Pair Encodings for Morphological Sequence Classification. In Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020), pages 76–86, Barcelona, Spain (Online). Association for Computational Linguistics.