Applications of Natural Language Processing in Bilingual Language Teaching: An Indonesian-English Case Study

Abstract

Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify. In this paper, we set out methodological considerations of using automated speech recognition to build a corpus of teacher speech in an Indonesian language classroom. Our preliminary results (64% word error rate) suggest these tools have the potential to speed data collection in this context. We provide practical examples of our data structure, details of our piloted computer-assisted processes, and fine-grained error analysis. Our study is informed and directed by genuine research questions and discussion in both the education and computational linguistics fields. We highlight some of the benefits and risks of using these emerging technologies to analyze the complex work of language teachers and in education more generally.

Cross-Domain Training for Goal-Oriented Conversational Agents

“…The third model was trained with Indonesian data only. Our preliminary result of 64% word error rate (WER) is high in comparison to mono-lingual ASR systems (Maxwelll-Smith et al, 2020). However, WERs from code-switch bilingual data (Biswas et al, 2019) were more similar to our WER, especially given our small amount of training data.…”

“…Using quantitative methods to understand language learning and teaching is difficult work as the 'transcription bottleneck' constrains the size of datasets. We found that using an automatic speech recognition (ASR) toolkit with a small set of training data is likely to speed data collection in this context (Maxwelll-Smith et al, 2020).…”

Fossicking in Dominant Language Teaching: Javanese and Indonesian ‘Low’ Varieties in Language Teaching Resources

“…This work also contributes to the development and scrutiny of Indonesian NLP resources (Nomoto, 2020) which require significant investment given the population of Indonesian exceeds 270 million (WorldBank, 2019). It shows how using the same computational tools regularly used in industry can be helpful to advance the education sector (Maxwell-Smith et al, 2020), even for languages with scarce resources.…”

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

“…Another field reaping the benefits of advancements in NLP is voice-based applications that have been deployed in the form of voice-controlled devices [42], teaching and answering [43] and [44], customer service agents [45], voice and multimedia mining [46], and virtual assistants [47][48][49]. Sentiment analysis has also become a popular tool on the internet to assess the feedback and the emotions or context of text written by humans.…”