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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Byte-Level Machine Reading Across Morphologically Varied Languages

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Tom Kenter

University of Amsterdam


Llion Jones

Google Research


Daniel Hewlett

Google


DOI:

10.1609/aaai.v32i1.12050


Abstract:

The machine reading task, where a computer reads a document and answers questions about it, is important in artificial intelligence research. Recently, many models have been proposed to address it. Word-level models, which have words as units of input and output, have proven to yield state-of-the-art results when evaluated on English datasets. However, in morphologically richer languages, many more unique words exist than in English due to highly productive prefix and suffix mechanisms. This may set back word-level models, since vocabulary sizes too big to allow for efficient computing may have to be employed. Multiple alternative input granularities have been proposed to avoid large input vocabularies, such as morphemes, character n-grams, and bytes. Bytes are advantageous as they provide a universal encoding format across languages, and allow for a small vocabulary size, which, moreover, is identical for every input language. In this work, we investigate whether bytes are suitable as input units across morphologically varied languages. To test this, we introduce two large-scale machine reading datasets in morphologically rich languages, Turkish and Russian. We implement 4 byte-level models, representing the major types of machine reading models and introduce a new seq2seq variant, called encoder-transformer-decoder. We show that, for all languages considered, there are models reading bytes outperforming the current state-of-the-art word-level baseline. Moreover, the newly introduced encoder-transformer-decoder performs best on the morphologically most involved dataset, Turkish. The large-scale Turkish and Russian machine reading datasets are released to public.

Topics: AAAI

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HOW TO CITE:

Tom Kenter||Llion Jones||Daniel Hewlett Byte-Level Machine Reading Across Morphologically Varied Languages Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Tom Kenter||Llion Jones||Daniel Hewlett Byte-Level Machine Reading Across Morphologically Varied Languages AAAI 2018, .

Tom Kenter||Llion Jones||Daniel Hewlett (2018). Byte-Level Machine Reading Across Morphologically Varied Languages. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Tom Kenter||Llion Jones||Daniel Hewlett. Byte-Level Machine Reading Across Morphologically Varied Languages. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Tom Kenter||Llion Jones||Daniel Hewlett. 2018. Byte-Level Machine Reading Across Morphologically Varied Languages. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Tom Kenter||Llion Jones||Daniel Hewlett. (2018) "Byte-Level Machine Reading Across Morphologically Varied Languages", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Tom Kenter||Llion Jones||Daniel Hewlett, "Byte-Level Machine Reading Across Morphologically Varied Languages", AAAI, p., 2018.

Tom Kenter||Llion Jones||Daniel Hewlett. "Byte-Level Machine Reading Across Morphologically Varied Languages". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Tom Kenter||Llion Jones||Daniel Hewlett. "Byte-Level Machine Reading Across Morphologically Varied Languages". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Tom Kenter||Llion Jones||Daniel Hewlett. Byte-Level Machine Reading Across Morphologically Varied Languages. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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