Scoping natural language processing in I ndonesian and M alay for education applications (2022)

Abstract: Indonesian and Malay are underrepresented in the development of natural language processing (NLP) technologies and available resources are difficult to find. A clear picture of existing work can invigorate and inform how researchers conceptualise worthwhile projects. Using an education sector project to motivate the study, we conducted a wide-ranging overview of Indonesian and Malay human language technologies and corpus work. We charted 657 included studies according to Hirschberg and Manning’s 2015 description of NLP, concluding that the field was dominated by exploratory corpus work, machine reading of text gathered from the Internet, and sentiment analysis. In this paper, we identify most published authors and research hubs, and make a number of recommendations to encourage future collaboration and efficiency within NLP in Indonesian and Malay.

## introduction limited natural language processing (nlp) resources currently available for indonesian and malay varieties do not reflect large speaker populations of these languages in indonesia, malaysia, and other south-east asian nations foot_0 . difficulties locating resources and existing work hinders progress in the field; it can result in duplicated or unnecessary work, clouding the ability of researchers to formulate useful research questions and study designs. since indonesian and malay varieties are closely related @xcite , connecting research and technologies developed for either language could provide useful insights and shortcuts for work in the other language. 2 these challenges restrict the impact that advances in nlp might have in the education sector in indonesia and malaysia, and in the teaching of these languages. ideally, teachers of indonesian or malay as a second or foreign language would draw on a wide range of human language technologies, machine learning methods, and corpus linguistics tools to enhance teaching and learning outcomes 3 . as part of a broader project investigating teacherspeech and materials for indonesian language teaching @xcite , the aims of this study were to scope the state of play of existing work in indonesian and malay nlp to assist in the formulation of realistic research goals, and to identify useful networks and resources. as such, our study draws on the notion of scoping work as "reconnaisance" @xcite , where the goal is to first determine what range of quantitative and/or qualitative evidence is available on a topic and then to chart, map, or otherwise represent this located evidence visually. our research questions were as follows: we begin by describing our search strategy and methods for charting 657 included studies by their topic and year of publication. we summarize existing literature reviews and notable work, graph most published authors and their affiliated research hubs, and describe recent educational applications of nlp. finally, we make 7 recommendations to build collaboration and efficiency in indonesian and malay nlp, highlighting our contribution to these goals. ## methods in order to capture a broad and rich picture of the recent literature, we applied a simplification of the systematic reviews and meta-analyses (prisma) for scoping reviews @xcite . generally, we aimed to provide a descriptive overview and visualization of the reviewed material without detailed critical appraisal of individual studies or synthesis of evidence from different studies @xcite . the review engaged with literature from many disciplines, including, but not limited to, computational linguistics, computer science, indonesian language teaching, and nlp. extensive consultations with research librarians resulted in a broad search strategy of the databases and terms (figure 2 ). both indonesian/malay and english search terms were used to maximize coverage. additional search terms focused on our interest in indonesian language teaching were used to mitigate the risk of missing relevant literature. we experienced significant problems identifying studies with the association for computational lin-guistics (acl) as their publisher. in searches via google scholar, scopus, and proquest; many acl publications (e.g., @xcite and @xcite ) were not returned foot_4 . we then added a direct acl anthology search to our database search list. unexplained behaviour in the acl anthology sort by 'year of publication' functionality cut results by over 400%, missing, for example, the aforementioned two relevant papers. to identify a reasonable portion of the work presented at acl events, we extended the set of identified records semi-automatically by manually opening and exporting studies from acl anthology searches. our inclusion criteria specified that studies be recent (published in 2016 or later), peer-reviewed, written in english, indonesian or malay, and relevant to our topic (work about other languages or unrelated to nlp was excluded). topical relevance was determined by a single reviewer (the first author of this paper) screening the title, abstract, and metadata of each identified study (@xmath0, 954) that was unique (@xmath1, 510) (figure 1 ). included studies were then classified according to hirschberg and manning's 2015 characterization of advances in nlp. we refer to these classifications in brief as: broad nlp; machine reading; machine translation; spoken dialogue systems; speaker state; and social media. we added a class -statistical work -to group work which primarily contributes corpus data or statistical and preprocessing work which stands at the foundation of most nlp. two reviewers (the first and last author of this paper) worked as a classification team, thereby assuring the quality of this 'light-touch' manual content analysis @xcite . in total, 80 of the 657 included studies (12.2%) were classified independently by both reviewers, including studies whose classification was perceived as uncertain by the first reviewer, as well as a random selection of further studies to increase confidence in reviewer agreement. reviewers' classification disagreements were resolved through reference to fulltext articles and discussion reaching consensus 5 . while many studies were classified as belonging to more than one 'grouping', the primary class was used in our analysis below. appendix b is sorted by the second and third classification levels to improve search-ability and provide further information. a search of titles and abstracts uncovered preexisting reviews. these reviews were screened in full text and their findings are outlined in our results section to complement the scoping or quantitative map of the field. literature outside our inclusion criteria which appeared highly relevant was retained separately for full-text review. unique names in the raw list of the top 50 mostpublished authors were manually normalized to prepare a publication-by-author count. manual identification of name variants for authors with many publications were identified by matching author initials, affiliations, and profiles where possible to create figure 5 . author affiliations for figure 6 were taken from the most recent study of a given author included in this overview. ## results a total of 657 from 1, 954 studies met our inclusion criteria. statistical and corpus work dominated throughout the last 5 years (from 2016 -figure 3 ). studies related to machine reading and sentiment analysis of online text such as news websites (included in 'speaker states') and social media (sentiment analysis comprises much of our 'social media' classification) were popular and showed growth (figure 4 ). the largest growth area was in 'speaker dialogue systems' with a relative boom in publishing in 2019 (26 studies). while our search terms were bilingual, the majority of studies that met our inclusion criteria were written in english or had an english title and abstract. the apparent stagnation of publications in 2020 (figure 4 ) with a rebound in the first half of 2021 (which our search covered) could be related to the context of the covid19 pandemic. however, as "country-specific variables play a significant role" @xcite in pandemic publication trends, this is very difficult to determine. as noted by hirschberg and manning in 2015, machine reading research makes use of the vast quantities of text available in the modern world while the mining of text from social media has "revolutionized the amount and types of information available today to nlp researchers". the presence of work in all classifications indicates indonesian and malay nlp has advanced from its 2015 state, when hirschberg and manning said it had "no such resources or systems available" @xcite . to complement the quantitative picture in figures 3 and 4 , we identified existing reviews and provide a summary below. a detailed full-text review of all articles identified in a given classification is needed to fully investigate progress in each respective field. ## discussion this study sheds significant light on the state of play and progress of indonesian and malay nlp. figure 6: author affiliation of the top 25 authors with most publications we make the following 7 recommendations and outline our related contributions to encourage collaboration and support appropriate investment in the development of indonesian and malay nlp, and its application in education. ## conclusion overall, this scoping study provides a baseline picture of indonesian and malay nlp. it shows an emerging research community engaged with the wide range of nlp advances identified in 2015 by hirschberg and manning. researchers in the field continue to experience difficulties in benchmarking performance without reliable evaluation techniques and reference datasets, re-engineering of loaned preprocessing tools from english nlp, and thankless tasks such as the creation of 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