Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting

James, Jesin; Yogarajan, Vithya; Shields, Isabella; Watson, C. J.; Keegan, Peter; Mahelona, Keoni; Jones, Peter-Lucas

doi:10.18653/v1/2022.findings-naacl.49

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Perhaps, using larger dictionaries can speed up the hand-correction stages of corpus building. Also, we note that the language detection models being developed using the labelled Hansard corpus [23] will help future researchers obtain automatically labelled corpora with less time than the one developed here. The Development of a Labelled te reo Māori-English Bilingual Corpus…”

Section: Discussionmentioning

confidence: 95%

“…This corpus is useful for developing language models, language detection, code-switching detection and similar language technology for Māori-English language pairs. A language detection model was trained using the labelled Hansard corpus, and the results are reported in [23]. The language detection model was tested in a formal context (such as political context in the Hansard database) and informal context (tweets from the [17]) with greater than 80% accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…We can now effectively analyse codeswitching in a 'political' context, but the effectiveness of the corpus in an informal context is unknown. Preliminary investigations into detecting codeswitching in tweets [23] have proven successful, but a detailed investigation is necessary.…”

Section: Discussionmentioning

confidence: 99%

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The development of a labelled te reo Māori–English bilingual database for language technology

James,

Shields,

Yogarajan

et al. 2023

Lang Resources & Evaluation

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Te reo Māori (referred to as Māori), New Zealand's indigenous language, is under-resourced in language technology. Māori speakers are bilingual, where Māori is code-switched with English. Unfortunately, there are minimal resources available for Māori language technology, language detection and code-switch detection between Māori-English pair. Both English and Māori use Roman-derived orthography making rule-based systems for detecting language and code-switching restrictive. Most Māori language detection is done manually by language experts. This research builds a Māori-English bilingual database of 66,016,807 words with word-level language annotation. The New Zealand Parliament Hansard debates reports were used to build the database. The language labels are assigned using language-specific rules and expert manual annotations. Words with the same spelling, but different meanings, exist for Māori and English. These words could not be categorised as Māori or English based on word-level language rules. Hence, manual annotations were necessary. An analysis reporting the various aspects of the database The Development of a Labelled te reo Māori-English Bilingual Corpus such as metadata, year-wise analysis, frequently occurring words, sentence length and N-grams is also reported. The database developed here is a valuable tool for future language and speech technology development for Aotearoa New Zealand. The methodology followed to label the database can also be followed by other low-resourced language pairs.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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The development of a labelled te reo Māori–English bilingual database for language technology

James,

Shields,

Yogarajan

et al. 2023

Lang Resources & Evaluation

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“…However, identifying monolingual and code-switch information from EKCS data problem needs to be addressed. James et al [25] provided an investigational evidence to demonstrat that the accuracy of cloud-based multilingual systems (Google and Microsoft Azure) is low when identifying Maori language. The proposed study shows that, the BiLSTM with bilingual embeddings to identify Maori-English code-switching points with an accuracy of 87%.…”

Section: Issn: 2088-8708 mentioning

confidence: 99%

Identification of monolingual and code-switch information from English-Kannada code-switch data

Chundi

Hulipalled

Simha

2023

IJECE

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<span lang="EN-US">Code-switching is a very common occurrence in social media communication, predominantly found in multilingual countries like India. Using more than one language in communication is known as code-switching or code-mixing. Some of the important applications of code-switch are machine translation (MT), shallow parsing, dialog systems, and semantic parsing. Identifying code-switch and monolingual information is useful for better communication in online networking websites. In this paper, we performed a character level n-gram approach to identify monolingual and code-switch information from English-Kannada social media data. We paralleled various machine learning techniques such as naïve Bayes (NB), support vector classifier (SVC), logistic regression (LR) and neural network (NN) on English-Kannada code-switch (EKCS) data. From the proposed approach, it is observed that the character level n-gram approach provides 1.8% to 4.1% of improvement in terms of Accuracy and 1.6% to 3.8% of improvement in F1-score. Also observed that SVC and NN techniques are outperformed in terms of accuracy (97.9%) and F1-score (98%) with character level n-gram.</span>

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“…The free-form medical text in the NZ-GP Harms dataset is predominantly written in English, but includes some te reo Māori. Automatic language detection in code-switched English-Māori text is an area of ongoing current research (James et al, 2022;Trye et al, 2022). For the purpose of this research, Māori language was identified manually.…”

Section: Understanding Bias In Text Datamentioning

confidence: 99%

Data and model bias in artificial intelligence for healthcare applications in New Zealand

Yogarajan¹,

Dobbie²,

Leitch³

et al. 2022

Front. Comput. Sci.

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IntroductionDevelopments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand.MethodsThis research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME.ResultsThis research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research.DiscussionsThis research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

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Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting

Cited by 6 publications

References 19 publications

The development of a labelled te reo Māori–English bilingual database for language technology

The development of a labelled te reo Māori–English bilingual database for language technology

Identification of monolingual and code-switch information from English-Kannada code-switch data

Data and model bias in artificial intelligence for healthcare applications in New Zealand

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