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International Conference on Text, Speech, and Dialogue

TSD 2019: Text, Speech, and Dialogue pp 58–68Cite as

Bidirectional LSTM Tagger for Latvian Grammatical Error Detection

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11697)

Abstract

This paper reports on the development of a grammar error labeling system for the Latvian language. We choose to label six error types that are crucial for understanding a text as noted in a survey by native Latvian speakers. The error types are the following: an incorrect use of a preposition, an incorrect agreement in a phrase, an incorrect verb form, an incorrect noun form, an incorrect choice of the definite/indefinite ending of an adjective, and a missing comma. For neural network model training, a large amount of error-annotated training data is required. We generate artificial errors in a correct text to cope with the lack of manually annotated data. As a bidirectional Long Short-Term Memory neural network algorithm is considered the best for erroneous word detection by several authors, we chose this architecture. We train several models – models labeling a single type of error and models labeling all six types of errors. The precision for all types of errors reaches 94.61%, the recall – 94.08%.

Keywords

  • Grammar errors
  • Neural network
  • Word embeddings

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Acknowledgment

The research has been supported by the European Regional Development Fund within the project “Neural Network Modelling for Inflected Natural Languages” No. 1.1.1.1/16/A/215.

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Authors and Affiliations

  1. Tilde, Vienibas gatve 75a, Riga, Latvia

    Daiga Deksne

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  1. Daiga Deksne
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Correspondence to Daiga Deksne .

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  1. University of West Bohemia, Pilsen, Czech Republic

    Kamil Ekštein

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Deksne, D. (2019). Bidirectional LSTM Tagger for Latvian Grammatical Error Detection. In: Ekštein, K. (eds) Text, Speech, and Dialogue. TSD 2019. Lecture Notes in Computer Science(), vol 11697. Springer, Cham. https://doi.org/10.1007/978-3-030-27947-9_5

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  • DOI: https://doi.org/10.1007/978-3-030-27947-9_5

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