Abstract
In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture. We propose four different input combination strategies for the encoder-decoder attention: serial, parallel, flat, and hierarchical. We evaluate our methods on tasks of multimodal translation and translation with multiple source languages. The experiments show that the models are able to use multiple sources and improve over single source baselines.- Anthology ID:
- W18-6326
- Volume:
- Proceedings of the Third Conference on Machine Translation: Research Papers
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 253–260
- Language:
- URL:
- https://aclanthology.org/W18-6326
- DOI:
- 10.18653/v1/W18-6326
- Bibkey:
- Cite (ACL):
- Jindřich Libovický, Jindřich Helcl, and David Mareček. 2018. Input Combination Strategies for Multi-Source Transformer Decoder. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 253–260, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Input Combination Strategies for Multi-Source Transformer Decoder (Libovický et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6326.pdf
Export citation
@inproceedings{libovicky-etal-2018-input,
title = "Input Combination Strategies for Multi-Source Transformer Decoder",
author = "Libovick{\'y}, Jind{\v{r}}ich and
Helcl, Jind{\v{r}}ich and
Mare{\v{c}}ek, David",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6326",
doi = "10.18653/v1/W18-6326",
pages = "253--260",
abstract = "In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture. We propose four different input combination strategies for the encoder-decoder attention: serial, parallel, flat, and hierarchical. We evaluate our methods on tasks of multimodal translation and translation with multiple source languages. The experiments show that the models are able to use multiple sources and improve over single source baselines.",
}
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%0 Conference Proceedings %T Input Combination Strategies for Multi-Source Transformer Decoder %A Libovický, Jindřich %A Helcl, Jindřich %A Mareček, David %Y Bojar, Ondřej %Y Chatterjee, Rajen %Y Federmann, Christian %Y Fishel, Mark %Y Graham, Yvette %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Specia, Lucia %Y Turchi, Marco %Y Verspoor, Karin %S Proceedings of the Third Conference on Machine Translation: Research Papers %D 2018 %8 October %I Association for Computational Linguistics %C Brussels, Belgium %F libovicky-etal-2018-input %X In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture. We propose four different input combination strategies for the encoder-decoder attention: serial, parallel, flat, and hierarchical. We evaluate our methods on tasks of multimodal translation and translation with multiple source languages. The experiments show that the models are able to use multiple sources and improve over single source baselines. %R 10.18653/v1/W18-6326 %U https://aclanthology.org/W18-6326 %U https://doi.org/10.18653/v1/W18-6326 %P 253-260
Markdown (Informal)
[Input Combination Strategies for Multi-Source Transformer Decoder](https://aclanthology.org/W18-6326) (Libovický et al., WMT 2018)
- Input Combination Strategies for Multi-Source Transformer Decoder (Libovický et al., WMT 2018)
ACL
- Jindřich Libovický, Jindřich Helcl, and David Mareček. 2018. Input Combination Strategies for Multi-Source Transformer Decoder. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 253–260, Brussels, Belgium. Association for Computational Linguistics.