Machine Translation for English--Inuktitut with Segmentation, Data Acquisition and Pre-Training

Christian Roest; Lukas Edman; Gosse Minnema; Kevin Kelly; Jennifer Spenader; Antonio Toral

Machine Translation for English--Inuktitut with Segmentation, Data Acquisition and Pre-Training

Christian Roest, Lukas Edman, Gosse Minnema, Kevin Kelly, Jennifer Spenader, Antonio Toral

Kunstmatige Intelligentie

Onderzoeksoutput › Academic › peer review

11 Citaten (Scopus)

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Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English--Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.

Originele taal-2	English
Titel	Proceedings of the Fifth Conference on Machine Translation (WMT)
Uitgeverij	Association for Computational Linguistics (ACL)
Pagina's	274-281
Aantal pagina's	8
Status	Published - nov.-2020
Evenement	Fifth Conference on Machine Translation - Online Duur: 19-nov.-2020 → 20-nov.-2020

Conference

Conference	Fifth Conference on Machine Translation
Verkorte titel	WMT20
Periode	19/11/2020 → 20/11/2020

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Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-TrainingFinal publisher's version, 200 KBLicentie: CC BY

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@inproceedings{ce2469633b304064ab65ae9e5e506c5e,

title = "Machine Translation for English--Inuktitut with Segmentation, Data Acquisition and Pre-Training",

abstract = "Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English--Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.",

author = "Christian Roest and Lukas Edman and Gosse Minnema and Kevin Kelly and Jennifer Spenader and Antonio Toral",

year = "2020",

month = nov,

language = "English",

pages = "274--281",

booktitle = "Proceedings of the Fifth Conference on Machine Translation (WMT)",

publisher = "Association for Computational Linguistics (ACL)",

note = "Fifth Conference on Machine Translation, WMT20 ; Conference date: 19-11-2020 Through 20-11-2020",

}

TY - GEN

T1 - Machine Translation for English--Inuktitut with Segmentation, Data Acquisition and Pre-Training

AU - Roest, Christian

AU - Edman, Lukas

AU - Minnema, Gosse

AU - Kelly, Kevin

AU - Spenader, Jennifer

AU - Toral, Antonio

PY - 2020/11

Y1 - 2020/11

N2 - Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English--Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.

AB - Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English--Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.

M3 - Conference contribution

SP - 274

EP - 281

BT - Proceedings of the Fifth Conference on Machine Translation (WMT)

PB - Association for Computational Linguistics (ACL)

T2 - Fifth Conference on Machine Translation

Y2 - 19 November 2020 through 20 November 2020

ER -

Machine Translation for English--Inuktitut with Segmentation, Data Acquisition and Pre-Training

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