Joan Byamugisha


2020

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Generating Varied Training Corpora in Runyankore Using a Combined Semantic and Syntactic, Pattern-Grammar-based Approach
Joan Byamugisha
Proceedings of the 13th International Conference on Natural Language Generation

Machine learning algorithms have been applied to achieve high levels of accuracy in tasks associated with the processing of natural language. However, these algorithms require large amounts of training data in order to perform efficiently. Since most Bantu languages lack the required training corpora because they are computationally under-resourced, we investigated how to generate a large varied training corpus in Runyankore, a Bantu language indigenous to Uganda. We found the use of a combined semantic and syntactic, pattern and grammar-based approach to be applicable to this purpose, and used it to generate one million sentences, both labelled and unlabelled, which can be applied as training data for machine learning algorithms. The generated text was evaluated in two ways: (1) assessing the semantics encoded in word embeddings obtained from the generated text, which showed correct word similarity; and (2) applying the labelled data to tasks such as sentiment analysis, which achieved satisfactory levels of accuracy.

2018

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Pluralizing Nouns across Agglutinating Bantu Languages
Joan Byamugisha | C. Maria Keet | Brian DeRenzi
Proceedings of the 27th International Conference on Computational Linguistics

Text generation may require the pluralization of nouns, such as in context-sensitive user interfaces and in natural language generation more broadly. While this has been solved for the widely-used languages, this is still a challenge for the languages in the Bantu language family. Pluralization results obtained for isiZulu and Runyankore showed there were similarities in approach, including the need to combine syntax with semantics, despite belonging to different language zones. This suggests that bootstrapping and generalizability might be feasible. We investigated this systematically for seven languages across three different Guthrie language zones. The first outcome is that Meinhof’s 1948 specification of the noun classes are indeed inadequate for computational purposes for all examined languages, due to non-determinism in prefixes, and we thus redefined the characteristic noun class tables of 29 noun classes into 53. The second main result is that the generic pluralizer achieved over 93% accuracy in coverage testing and over 94% on a random sample. This is comparable to the language-specific isiZulu and Runyankore pluralizers.

2017

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Evaluation of a Runyankore grammar engine for healthcare messages
Joan Byamugisha | C. Maria Keet | Brian DeRenzi
Proceedings of the 10th International Conference on Natural Language Generation

Natural Language Generation (NLG) can be used to generate personalized health information, which is especially useful when provided in one’s own language. However, the NLG technique widely used in different domains and languages—templates—was shown to be inapplicable to Bantu languages, due to their characteristic agglutinative structure. We present here our use of the grammar engine NLG technique to generate text in Runyankore, a Bantu language indigenous to Uganda. Our grammar engine adds to previous work in this field with new rules for cardinality constraints, prepositions in roles, the passive, and phonological conditioning. We evaluated the generated text with linguists and non-linguists, who regarded most text as grammatically correct and understandable; and over 60% of them regarded all the text generated by our system to have been authored by a human being.

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Toward an NLG System for Bantu languages: first steps with Runyankore (demo)
Joan Byamugisha | C. Maria Keet | Brian DeRenzi
Proceedings of the 10th International Conference on Natural Language Generation

There are many domain-specific and language-specific NLG systems, of which it may be possible to adapt to related domains and languages. The languages in the Bantu language family have their own set of features distinct from other major groups, which therefore severely limits the options to bootstrap an NLG system from existing ones. We present here our first proof-of-concept application for knowledge-to-text NLG as a plugin to the Protege 5.x ontology development system, tailored to Runyankore, a Bantu language indigenous to Uganda. It comprises a basic annotation model for linguistic information such as noun class, an implementation of existing verbalisation rules and a CFG for verbs, and a basic interface for data entry.

2016

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Tense and Aspect in Runyankore Using a Context-Free Grammar
Joan Byamugisha | C. Maria Keet | Brian DeRenzi
Proceedings of the 9th International Natural Language Generation conference