Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering

Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 119–130 Brussels, Belgium, October 31 - November 1, 2018. c⃝2018 Association for Computational Linguistics 119 Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural based Question Answering Deepak Gupta‡, Pabitra Lenka†∗, Asif Ekbal‡, Pushpak Bhattacharyya‡ ‡Indian Institute of Technology Patna, India †International Institute of Information Technology Bhubaneswar, India ‡{deepak.pcs16, asif, pb}@iitp.ac.in †pabitra.lenka18@gmail.com Abstract Existing research on question answering (QA) and comprehension reading (RC) are mainly focused on the resource-rich language like English. In recent times, there has been a rapid growth of multi-lingual contents on the web, and this has posed several challenges to the existing QA systems. Code-mixing is one such challenge that makes the task even more complex. In this paper, we propose a linguistically motivated technique for code- mixed question generation (CMQG) and a neu- ral network based architecture for code-mixed question answering (CMQA). For evaluation, we manually create the code-mixed questions for Hindi-English language pair. In order to show the effectiveness of our neural net- work based CMQA technique, we utilize two benchmark datasets, viz. SQuAD and MMQA. Experiments show that our proposed model achieves encouraging performance on CMQG and CMQA. 1 Introduction The people who are multilingual in nature often switch back and forth between their native lan- guages and the foreign (popular) languages to ex- press themselves on the web. This is very common nowadays, particularly when people express their opinions (or making any communication) through various social media platforms. This phenomenon of embedding the morphemes, words, phrases, etc. of one language into another is popularly termed as code-mixing (CM) (Myers-Scotton, 1997, 2002). The recent study (Safran, 2015) has uncovered that users frequently use question patterns, namely ‘how’ (38%), ‘why’ (24%), ‘where’ (15%), ‘what’ (11%), and ‘which’ (12%) in their queries as op- posed to a ‘statement query’. ∗Work carried out during the internship at IIT Patna Presently, the search engines have become intelligent and are capable enough to pro- vide precise answer to a natural language query/question1.Several virtual assistants such as Siri, Cortana, Alexa, Google Assistant, etc are also equipped with these facilities. However, these search engines and virtual assistants are efficient only in handling the queries written in English. Let us consider the following two representations (English and code-mixed) of the same question. (i) Q: “Who is the foreign secretary of USA?” (ii) Q: “USA ke foreign secretary koun hai?” (Trans:“Who is the foreign secretary of USA?”) Search engines are able to provide the exact answer to the first question. It is to be noted that although both the questions are same, the search engine is unable to return the exact answer for the second question, which is code-mixed in nature. It rather returns the top-most relevant web pages. In this paper, we propose a framework for code- mixed question generation (CMQG) as well as code-mixed question answering (CMQA) involv- ing English and Hindi. Firstly, we propose a lin- guistically motivated technique for generating the code-mixed …

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