Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation
Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, Muthusamy Chelliah
Abstract
Multilingual chatbots are the need of the hour for modern business. There is increasing demand for such systems all over the world. A multilingual chatbot can help to connect distant parts of the world together, without sharing a common language. We participated in WMT22 Chat Translation Shared Task. In this paper, we report descriptions of methodologies used for participation. We submit outputs from multi-encoder based transformer model, where one encoder is for context and another for source utterance. We consider one previous utterance as context. We obtain COMET scores of 0.768 and 0.907 on English-to-German and German-to-English directions, respectively. We submitted outputs without using context at all, which generated worse results in English-to-German direction. While for German-to-English, the model achieved a lower COMET score but slightly higher chrF and BLEU scores. Further, to understand the effectiveness of the context encoder, we submitted a run after removing the context encoder during testing and we obtain similar results.- Anthology ID:
- 2022.wmt-1.90
- Original:
- 2022.wmt-1.90v1
- Version 2:
- 2022.wmt-1.90v2
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 949–954
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.90
- DOI:
- Bibkey:
- Cite (ACL):
- Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, and Muthusamy Chelliah. 2022. Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 949–954, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation (Gain et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.90.pdf
Export citation
@inproceedings{gain-etal-2022-investigating,
title = "Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation",
author = "Gain, Baban and
Appicharla, Ramakrishna and
Chennabasavaraj, Soumya and
Garera, Nikesh and
Ekbal, Asif and
Chelliah, Muthusamy",
editor = {Koehn, Philipp and
Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.90",
pages = "949--954",
abstract = "Multilingual chatbots are the need of the hour for modern business. There is increasing demand for such systems all over the world. A multilingual chatbot can help to connect distant parts of the world together, without sharing a common language. We participated in WMT22 Chat Translation Shared Task. In this paper, we report descriptions of methodologies used for participation. We submit outputs from multi-encoder based transformer model, where one encoder is for context and another for source utterance. We consider one previous utterance as context. We obtain COMET scores of 0.768 and 0.907 on English-to-German and German-to-English directions, respectively. We submitted outputs without using context at all, which generated worse results in English-to-German direction. While for German-to-English, the model achieved a lower COMET score but slightly higher chrF and BLEU scores. Further, to understand the effectiveness of the context encoder, we submitted a run after removing the context encoder during testing and we obtain similar results.",
}
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%0 Conference Proceedings %T Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation %A Gain, Baban %A Appicharla, Ramakrishna %A Chennabasavaraj, Soumya %A Garera, Nikesh %A Ekbal, Asif %A Chelliah, Muthusamy %Y Koehn, Philipp %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F gain-etal-2022-investigating %X Multilingual chatbots are the need of the hour for modern business. There is increasing demand for such systems all over the world. A multilingual chatbot can help to connect distant parts of the world together, without sharing a common language. We participated in WMT22 Chat Translation Shared Task. In this paper, we report descriptions of methodologies used for participation. We submit outputs from multi-encoder based transformer model, where one encoder is for context and another for source utterance. We consider one previous utterance as context. We obtain COMET scores of 0.768 and 0.907 on English-to-German and German-to-English directions, respectively. We submitted outputs without using context at all, which generated worse results in English-to-German direction. While for German-to-English, the model achieved a lower COMET score but slightly higher chrF and BLEU scores. Further, to understand the effectiveness of the context encoder, we submitted a run after removing the context encoder during testing and we obtain similar results. %U https://aclanthology.org/2022.wmt-1.90 %P 949-954
Markdown (Informal)
[Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation](https://aclanthology.org/2022.wmt-1.90) (Gain et al., WMT 2022)
- Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation (Gain et al., WMT 2022)
ACL
- Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, and Muthusamy Chelliah. 2022. Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 949–954, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.