Multilingual Datasets for Augmentative and Alternative Communication

Keith Vertanen

Multilingual Datasets for Augmentative and Alternative Communication

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Abstract

There is a lack of text resources for building predictive Augmentative and Alternative (AAC) interfaces in many languages. We share a compilation of four multilingual datasets in hopes of improving the situation. We started with four English datasets designed to match the conversational-style text AAC users may want to produce in person-to-person conversations. Next, we used a large language model (LLM) to correct errors and augment the original English data. Finally, we used Google’s translation LLM to convert the datasets’ 9,154 individual communications and 37,486 dialogue turns into 33 other languages.

Anthology ID:: 2026.mrl-1.31
Volume:: Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026)
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: David Ifeoluwa Adelani, Jesujoba Alabi, Catherine Arnett, Tyler Chang, C.M. Downey, Omer Goldman, Senyu Li, Lester James Validad Miranda, Jirui Qi, David Stap, Genta Indra Winata, Xinyu Crystina Zhang
Venues:: MRL | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 386–393
Language:
URL:: https://aclanthology.org/2026.mrl-1.31/
DOI:
Bibkey:
Cite (ACL):: Keith Vertanen. 2026. Multilingual Datasets for Augmentative and Alternative Communication. In Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026), pages 386–393, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Multilingual Datasets for Augmentative and Alternative Communication (Vertanen, MRL 2026)
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PDF:: https://aclanthology.org/2026.mrl-1.31.pdf

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@inproceedings{vertanen-2026-multilingual,
    title = "Multilingual Datasets for Augmentative and Alternative Communication",
    author = "Vertanen, Keith",
    editor = "Adelani, David Ifeoluwa  and
      Alabi, Jesujoba  and
      Arnett, Catherine  and
      Chang, Tyler  and
      Downey, C.M.  and
      Goldman, Omer  and
      Li, Senyu  and
      Miranda, Lester James Validad  and
      Qi, Jirui  and
      Stap, David  and
      Winata, Genta Indra  and
      Zhang, Xinyu Crystina",
    booktitle = "Proceedings of the 6th Workshop on Multilingual Representation Learning ({MRL} 2026)",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.mrl-1.31/",
    pages = "386--393",
    ISBN = "979-8-89176-469-9",
    abstract = "There is a lack of text resources for building predictive Augmentative and Alternative (AAC) interfaces in many languages. We share a compilation of four multilingual datasets in hopes of improving the situation. We started with four English datasets designed to match the conversational-style text AAC users may want to produce in person-to-person conversations. Next, we used a large language model (LLM) to correct errors and augment the original English data. Finally, we used Google{'}s translation LLM to convert the datasets' 9,154 individual communications and 37,486 dialogue turns into 33 other languages."
}

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%0 Conference Proceedings
%T Multilingual Datasets for Augmentative and Alternative Communication
%A Vertanen, Keith
%Y Adelani, David Ifeoluwa
%Y Alabi, Jesujoba
%Y Arnett, Catherine
%Y Chang, Tyler
%Y Downey, C. M.
%Y Goldman, Omer
%Y Li, Senyu
%Y Miranda, Lester James Validad
%Y Qi, Jirui
%Y Stap, David
%Y Winata, Genta Indra
%Y Zhang, Xinyu Crystina
%S Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-469-9
%F vertanen-2026-multilingual
%X There is a lack of text resources for building predictive Augmentative and Alternative (AAC) interfaces in many languages. We share a compilation of four multilingual datasets in hopes of improving the situation. We started with four English datasets designed to match the conversational-style text AAC users may want to produce in person-to-person conversations. Next, we used a large language model (LLM) to correct errors and augment the original English data. Finally, we used Google’s translation LLM to convert the datasets’ 9,154 individual communications and 37,486 dialogue turns into 33 other languages.
%U https://aclanthology.org/2026.mrl-1.31/
%P 386-393

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Markdown (Informal)

[Multilingual Datasets for Augmentative and Alternative Communication](https://aclanthology.org/2026.mrl-1.31/) (Vertanen, MRL 2026)

Multilingual Datasets for Augmentative and Alternative Communication (Vertanen, MRL 2026)

ACL

Keith Vertanen. 2026. Multilingual Datasets for Augmentative and Alternative Communication. In Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026), pages 386–393, Budapest, Hungary. Association for Computational Linguistics.