Ontology verbalization in agglutinating Bantu languages: a study of Runyankore and its generalizability

Natural Language Generation (NLG) systems have been developed to generate text in multiple domains, including personalized patient information. However, their application is limited in Africa because they generate text in English, yet indigenous languages are still predominantly spoken throughout the continent, especially in rural areas. The existing healthcare NLG systems cannot be reused for Bantu languages due to the complex grammatical structure, nor can the generated text be used in machine translation systems for Bantu languages because they are computationally under-resourced. This research aimed to verbalize ontologies in agglutinating Bantu languages. We had four research objectives: (1) noun pluralization and verb conjugation in Runyankore; (2) Runyankore verbalization patterns for the selected description logic constructors; (3) combining the pluralization, conjugation, and verbalization components to form a Runyankore grammar engine; and (4) generalizing the Runyankore and isiZulu approaches to ontology verbalization to other agglutinating Bantu languages. We used an approach that combines morphology with syntax and semantics to develop a noun pluralizer for Runyankore, and used Context-Free Grammars (CFGs) for verb conjugation. We developed verbalization algorithms for eight constructors in a description logic. We then combined these components into a grammar engine developed as a Protégé5X plugin. The investigation into generalizability used the bootstrap approach, and investigated bootstrapping for languages in the same language zone (intra-zone bootstrappability) and languages across language zones (inter-zone bootstrappability). We obtained verbalization patterns for Luganda and isiXhosa, in the same zones as Runyankore and isiZulu respectively, and chiShona, Kikuyu, and Kinyarwanda from different zones, and used the bootstrap metric that we developed to identify the most efficient source—target bootstrap pair. By regrouping Meinhof’s noun class system we were able to eliminate non-determinism during computation, and this led to the development of a generic noun pluralizer. We also showed that CFGs can conjugate verbs in the five additional languages. Finally, we proposed the architecture for an API that could be used to generate text in agglutinating Bantu languages. Our research provides a method for surface realization for an under-resourced and grammatically complex family of languages, Bantu languages. We leave the development of a complete NLG system based on the Runyankore grammar engine and of the API as areas for future work

ToCT: A task ontology to manage complex templates

Natural language interfaces are a well-known approach to grant non-experts access to semantic web technologies. A number of such systems use simple templates to achieve that for English and more elab-orate solutions for other languages. They keep being designed from scratch in an ad hoc manner, since there is no shared conceptualisation of simple templates and there is no model that is formalised using a Semantic Web language to apply the techniques to itself. We aim to address this by proposing a general-purpose solution in the form of a novel model for templates, formalised as a task ontology in OWL,calledToCT. We used it to develop an ontology-driven text generator for isiZulu, a morphologically-rich language, to test its capabilities. The generator verbalises the TBox of an ontology as validationq uestions. This evaluation showed that the task ontology is sufficiently expressive for the template design, which was subsequently verified with user evaluations who judged the texts positivel

Surface realization architecture for low-resourced African languages

There has been growing interest in building surface realization systems to support the automatic generation of text in African languages. Such tools focus on converting abstract representations of meaning to text. Since African languages are low-resourced, economical use of resources and general maintainability are key considerations. However, there is no existing surface realizer architecture that possesses most of the maintainability characteristics (e.g., modularity, reusability, and analyzability) that will lead to maintainable software that can be used for the languages. Moreover, there is no consensus surface realization architecture created for other languages that can be adapted for the languages in question. In this work, we solve this by creating a novel surface realizer architecture suitable for low-resourced African languages that abide by the features of maintainable software. Its design comes after a granular analysis, classification, and comparison of the architectures used by 77 existing NLG systems. We compare our architecture to existing architectures and show that it supports the most features of a maintainable software product.Hasso Plattner Institute for Digital Engineering through the HPI Research School at UCT and the National Research Foundation (NRF) of South Africahttps://dl.acm.org/journal/tallipInformatic

A model for verbalising relations with roles in multiple languages

Natural language renderings of ontologies facilitate communication with domain experts. While for ontologies with terms in English this is fairly straightforward, it is problematic for grammatically richer languages due to conjugation of verbs, an article that may be dependent on the preposition, or a preposition that modifies the noun. There is no systematic way to deal with such `complex' names of OWL object properties, or their verbalisation with existing language models for annotating ontologies. The modifications occur only when the object performs some {\em role} in a relation, so we propose a conceptual model that can handle this. This requires reconciling the standard view with relational expressions to a positionalist view, which is included in the model and in the formalisation of the mapping between the two. This eases verbalisation and it allows for a more precise representation of the knowledge, yet is still compatible with existing technologies. We have implemented it as a Prot\'eg\'e plugin and validated its adequacy with several languages that need it, such as German and isiZulu

Evaluation of a Runyankore grammar engine for healthcare messages

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

A classification of grammar-infused templates for ontology and model verbalisation

Involving domain-experts in the development, maintenance, and use of knowledge organisation systems can be made easier through the introduction of easy-to-use interfaces that are based on natural language. Well resourced languages make use of natural language generation techniques to provide such interfaces. In particular, they often make use of templates combined with computational grammar rules to generate grammatically complex text. However, there is no model of pairing templates and computational grammar rules to ensure suitability for less-resourced languages. These languages require a modular design that ensures grammar detachability so as to allow grammar re-use across domains and applications. In this paper, we present a model and classification scheme for grammar-infused templates suited for less-resourced languages and classify existing systems that make use of them. We have found that of the 15 systems that pair templates and grammar rules, and their 11 distinct template types, 13 have support for detachable grammars

User Interfaces to the Web of Data based on Natural Language Generation

We explore how Virtual Research Environments based on Semantic Web technologies support research interactions with RDF data in various stages of corpus-based analysis, analyze the Web of Data in terms of human readability, derive labels from variables in SPARQL queries, apply Natural Language Generation to improve user interfaces to the Web of Data by verbalizing SPARQL queries and RDF graphs, and present a method to automatically induce RDF graph verbalization templates via distant supervision

Zināšanās bāzētu un korpusā bāzētu metožu kombinētā izmantošanas mašīntulkošanā

ANOTĀCIJA. Mašīntulkošanas (MT) sistēmas tiek būvētas izmantojot dažādas metodes (zināšanās un korpusā bāzētas). Zināšanās bāzēta MT tulko tekstu, izmantojot cilvēka rakstītus likumus. Korpusā bāzēta MT izmanto no tulkojumu piemēriem automātiski izgūtus modeļus. Abām metodēm ir gan priekšrocības, gan trūkumi. Šajā darbā tiek meklēta kombināta metode MT kvalitātes uzlabošanai, kombinējot abas metodes. Darbā tiek pētīta metožu piemērotība latviešu valodai, kas ir maza, morfoloģiski bagāta valoda ar ierobežotiem resursiem. Tiek analizētas esošās metodes un tiek piedāvātas vairākas kombinētās metodes. Metodes ir realizētas un novērtētas, izmantojot gan automātiskas, gan cilvēka novērtēšanas metodes. Faktorēta statistiskā MT ar zināšanās balstītu morfoloģisko analizatoru ir piedāvāta kā perspektīvākā. Darbā aprakstīts arī metodes praktiskais pielietojums. Atslēgas vārdi: mašīntulkošana (MT), zināšanās balstīta MT, korpusā balstīta MT, kombinēta metodeABSTRACT. Machine Translation (MT) systems are built using different methods (knowledge-based and corpus-based). Knowledge-based MT translates text using human created rules. Corpus-based MT uses models which are automatically built from translation examples. Both methods have their advantages and disadvantages. This work aims to find a combined method to improve the MT quality combining both methods. An applicability of the methods for Latvian (a small, morphologically rich, under-resourced language) is researched. The existing MT methods have been analyzed and several combined methods have been proposed. Methods have been implemented and evaluated using an automatic and human evaluation. The factored statistical MT with a rule-based morphological analyzer is proposed to be the most promising. The practical application of methods is described. Keywords: Machine Translation (MT), Rule-based MT, Statistical MT, Combined approac