BELA : Bot for E nglish Language Acquisition (2022)

Abstract: In this paper, we introduce a conversational agent (chatbot) for Hindi-speaking youth called BELA—Bot for English Language Acquisition. Developed for young underprivileged students at an Indian non-profit, the agent supports both Hindi and Hinglish (code-switched Hindi and English, written primarily with English orthography) utterances. BELA has two interaction modes: a question-answering mode for classic English language learning tasks like word meanings, translations, reading passage comprehensions, etc., and an open-domain dialogue system mode to allow users to practice their language skills. We present a high-level overview of the design of BELA, including the implementation details and the preliminary results of our early prototype. We also report the challenges in creating an English-language learning chatbot for a largely Hindi-speaking population.

## introduction our paper introduces 'bela', bot for english language acquisition, an application of conversational agents (chatbots) in the domain of english language learning. bela is developed for young underprivileged students at an indian non-profit called udayan care. we were motivated to develop bela for the students at udayan care because we observed a lack of volunteer support by the non-profit's english language mentors, leading to a halt in the mentees' second-language acquisition. therefore, bela is intended to emulate an english language mentor for the udayan care students, and support the non-profit's volunteers by reducing their workload. our conversational agent has two interaction modes: a retrieval mode to facilitate questionanswering on classic english tasks like word meanings, translations, reading passage comprehensions, three tenets have governed the design of bela: some challenges to developing bela were the lack of data for intent classification and dialogue management, and a lack of a database of reading passages and english videos levelled by learnerproficiency level. our paper discusses how we overcame these challenges. organization: the rest of the paper is organized as follows: we begin with a high-level overview of the tutor bot and the buddy bot, the two interaction modes of our conversational agent (section 2); we next discuss the natural language understanding and dialogue management strategy of our conversational agent (section 3); further, we discuss in detail the first prototype implementation of the agent (section 4); we next present related work (section 5), and close with concluding remarks (section 6) ## interaction modes our conversational agent has two interaction modes: an english language question-answering mode called the tutor bot, and a general chit-chat mode called the buddy bot. ## natural language understanding & dialogue management the natural language understanding and dialogue management system of our agent is simple and intuitive. ## conclusion bela is our first step toward making personalised second-language acquisition more accessible to hindi-speaking learners. our future work would focus on increasing the range of english learning tasks that bela can assist with, improving the hinglish language understanding pipeline and making the dialogue management system more robust to failure.