Towards the First Named Entity Recognition of I nuktitut for an Improved Machine Translation (2023)

Abstract: Named Entity Recognition is a crucial step to ensure good quality performance of several Natural Language Processing applications and tools, including machine translation and information retrieval. Moreover, it is considered as a fundamental module of many Natural Language Understanding tasks such as question-answering systems. This paper presents a first study on NER for an under-represented Indigenous Inuit language of Canada, Inuktitut, which lacks linguistic resources and large labeled data. Our proposed NER model for Inuktitut is built by transferring linguistic characteristics from English to Inuktitut, based on either rules or bilingual word embeddings. We provide an empirical study based on a comparison with the state of the art models and as well as intrinsic and extrinsic evaluations. In terms of Recall, Precision and F-score, the obtained results show the effectiveness of the proposed NER methods. Furthermore, it improved the performance of Inuktitut-English Neural Machine Translation.

## introduction in recent years, artificial intelligence has recently gained much attention in research and development, particularly when applied to the field of natural language processing (nlp) and human language technologies. this paper focuses on named entities recognition (ner), one of the crucial tasks in several nlp applications and resources. the latter consists in identifying and classifying the names of the specified categories according to predefined semantic types, such as, the names of people, the place, the organization and the numerical expressions, in particular, the currency, the date and the percentage @xcite . ner being among the most important tasks of nlp; however, the success of such models is highly dependent on the amount of available annotated data, which is scarce and difficult to obtain. furthermore, be-cause of the unavailability of annotated data, it is more difficult to apply these nlp methods to low resourced languages and domains, such as inuktitut, one of the main indigenous languages in north america and the canadian arctic, and part of a larger inuit language family, stretching from alaska to greenland foot_0 . according to unesco, 75% of indigenous languages are threatened with extinction, and language loss is currently occurring at an accelerating rate due to globalization. therefore, the revitalization of endangered languages has become an important task for the preservation of cultural diversity on our planet @xcite . in our research, we are interested in inuktitut. our main objective in this framework is to address the linguistic challenges and to detect named entities for this language through the following contributions: also, this work would contribute to the preservation and revitalization of the inuktitut as well as other (related) indigenous languages. the current paper is organised as follows: section 2 introduces indigenous knowledge including research on several domains such as language, culture, and identity, as well as the relevant works in ner domain. section 3 presents our methodology via several methods to deal with ner task, and an empirical case study of the machine translation task including the ner. experiments and evaluations are presented in sections 4 and 5. finally, section 6 gives some conclusions and future research directions. ## a dive into indigenous research and nlp since 2020, new directions for indigenous research were put in place by canada research coordinating committee 2 , to help indigenous peoples and communities partner with research fields, to support and to encourage them to conduct their own research 3 . as with any culture, language is an essential part of indigenous knowledge, it is also one of the important disciplines of indigenous research in canada. indigenous languages in canada have changed and evolved over time and over generations. like all languages, they carry literary, cultural, traditional, but also historical values @xcite . one of the particularities of the indigenous languages of canada is that, for some, they are not spoken elsewhere in the world and are specific to canada 4 . as a result, these languages must be preserved because they represent one of the linguistic and therefore cultural riches of canada. it is mentioned in the canadian statistics foot_1 , that the 2016 census recorded more than 70 indigenous languages divided into 12 language families. the inuit languages are considered the second indigenous language family with the largest number of speakers after the algonquian languages. the most used language in this linguistic family is inuktitut, mainly spoken in nunavut and quebec. in our research, we are particularly interested in this language, rich and at the same time morphologicall complex, as presented in the following section. ## methodology a promising solution for ner task in low-resource languages, without annotated data, is from richresource languages using unsupervised transfer models. given the unavailable annotated data for the inuktitut and the availability of the latter in english, the main idea of our approach is to transfer the linguistic features of english to inuktitut. however, the main challenge of this method is the mapping of lexical items between languages. indeed, this is due to differences in words and word order across languages. we present, here, two approaches. the first approach consists of transferring the ner annotation from english to inuktitut by combining rules using a morphological analyzer with word alignment; while the second approach is based on the bilingual word embeddings using a bilingual dictionary (english-inuktitut) that we built. ## evaluations to evaluate our proposed method, we used automatic evaluation metrics such as, recall, precision, f1, alignment error rate (aer), bleu score for bilingual evaluation understudy @xcite with sacrebleu @xcite , chrf++ @xcite for calculating character n-gram f-score, and translation error rate (ter). ## conclusion and perspective in this paper, we have built a named entity recognition system for inuktitut, an inuit language of canada. counted among the four major dialectal groups of inuit languages, inuktitut is written using the native canadian syllabary. indeed, it is a lowresource indigenous language that has no labeled data for ner; which presents a great challenge to the construction of the first ner system. also, the inuktitut language, being a polysynthetic language, has a particular grammar and fairly complex word compositions that differentiate it from other languages. to overcome these problems, the main idea of our approach is to use english, given that it is a language rich in resources and that has labeled data for ner and a parallel inuktitut-english corpus is available. thus, in this paper, we built a model capable of detecting named entities in inuktitut, by transferring linguistic characteristics from english to inuktitut. in addition to being the first research on named entities recognition for inuktitut indigenous language, this project contributes to the preservation of this language and its culture. furthermore, by building a knowledge base in the inuktitut language involving named entities, this will contribute to the realization of future works that affects other nlp sub-tasks , such as information retrieval, machine translation or question0answering systems. as a future research, we aim to integrate knowledge bases such as those related to toponymy and data from indigenous knowledge in training word embeddings and improving the performance of our systems (ner and nmt). in addition, we aim to emphasize a differentiation between named entities of inuktitut origin (such as the names of people and places) and those borrowed. all with the aim of pursuing collaborations with an indigenous community in nunavut whose mother tongue is inuktitut.