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        <title>Lattice-Based Transformer Encoder for Neural Machine Translation</title>
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        <namePart type="given">Fengshun</namePart>
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        <namePart type="given">Jiangtong</namePart>
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    <abstract>Neural machine translation (NMT) takes deterministic sequences for source representations. However, either word-level or subword-level segmentations have multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. We hypothesize that the diversity in segmentations may affect the NMT performance. To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training. We propose two methods: 1) lattice positional encoding and 2) lattice-aware self-attention. These two methods can be used together and show complementary to each other to further improve translation performance. Experiment results show superiorities of lattice-based encoders in word-level and subword-level representations over conventional Transformer encoder.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1298</identifier>
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%0 Conference Proceedings
%T Lattice-Based Transformer Encoder for Neural Machine Translation
%A Xiao, Fengshun
%A Li, Jiangtong
%A Zhao, Hai
%A Wang, Rui
%A Chen, Kehai
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F xiao-etal-2019-lattice
%X Neural machine translation (NMT) takes deterministic sequences for source representations. However, either word-level or subword-level segmentations have multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. We hypothesize that the diversity in segmentations may affect the NMT performance. To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training. We propose two methods: 1) lattice positional encoding and 2) lattice-aware self-attention. These two methods can be used together and show complementary to each other to further improve translation performance. Experiment results show superiorities of lattice-based encoders in word-level and subword-level representations over conventional Transformer encoder.
%R 10.18653/v1/P19-1298
%U https://aclanthology.org/P19-1298/
%U https://doi.org/10.18653/v1/P19-1298
%P 3090-3097

Markdown (Informal)

[Lattice-Based Transformer Encoder for Neural Machine Translation](https://aclanthology.org/P19-1298/) (Xiao et al., ACL 2019)

• Lattice-Based Transformer Encoder for Neural Machine Translation (Xiao et al., ACL 2019)

ACL

• Fengshun Xiao, Jiangtong Li, Hai Zhao, Rui Wang, and Kehai Chen. 2019. Lattice-Based Transformer Encoder for Neural Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3090–3097, Florence, Italy. Association for Computational Linguistics.