Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach

[EN] Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions.The work of the third author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bandyopadhyay, S. (2018). Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach. Journal of Intelligent & Fuzzy Systems. 34(5):2959-2969. https://doi.org/10.3233/JIFS-169481S2959296934

MSIR@FIRE: A Comprehensive Report from 2013 to 2016

[EN] India is a nation of geographical and cultural diversity where over 1600 dialects are spoken by the people. With the technological advancement, penetration of the internet and cheaper access to mobile data, India has recently seen a sudden growth of internet users. These Indian internet users generate contents either in English or in other vernacular Indian languages. To develop technological solutions for the contents generated by the Indian users using the Indian languages, the Forum for Information Retrieval Evaluation (FIRE) was established and held for the first time in 2008. Although Indian languages are written using indigenous scripts, often websites and user-generated content (such as tweets and blogs) in these Indian languages are written using Roman script due to various socio-cultural and technological reasons. A challenge that search engines face while processing transliterated queries and documents is that of extensive spelling variation. MSIR track was first introduced in 2013 at FIRE and the aim of MSIR was to systematically formalize several research problems that one must solve to tackle the code mixing in Web search for users of many languages around the world, develop related data sets, test benches and most importantly, build a research community focusing on this important problem that has received very little attention. This document is a comprehensive report on the 4 years of MSIR track evaluated at FIRE between 2013 and 2016.Somnath Banerjee and Sudip Kumar Naskar are supported by Media Lab Asia, MeitY, Government of India, under the Visvesvaraya PhD Scheme for Electronics & IT. The work of Paolo Rosso was partially supported by the MISMIS research project PGC2018-096212-B-C31 funded by the Spanish MICINN.Banerjee, S.; Choudhury, M.; Chakma, K.; Kumar Naskar, S.; Das, A.; Bandyopadhyay, S.; Rosso, P. (2020). MSIR@FIRE: A Comprehensive Report from 2013 to 2016. 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Modeling Classifier for Code Mixed Cross Script Questions.

ABSTRACT With a boom in the internet, the social media text had been increasing day by day and the user generated content (such as tweets and blogs) in Indian languages are written using Roman script due to various socio-cultural and technological reasons. A majority of these posts are multilingual in nature and many involve code mixing where lexical items and grammatical features from two languages appear in one sentence. Focusing on this current multilingual scenario, code-mixed cross-script (i.e., non-native script) data gives rise to a new problem and presents serious challenges to automatic Question Answering (QA) and for this question classification will be required which is an important step towards QA. This paper proposes an approach to handle cross script question classification as it is an important task of question analysis which detects the category of the question

Learning to represent, categorise and rank in community question answering

The task of Question Answering (QA) is arguably one of the oldest tasks in Natural Language Processing, attracting high levels of interest from both industry and academia. However, most research has focused on factoid questions, e.g. Who is the president of Ireland? In contrast, research on answering non-factoid questions, such as manner, reason, difference and opinion questions, has been rather piecemeal. This was largely due to the absence of available labelled data for the task. This is changing, however, with the growing popularity of Community Question Answering (CQA) websites, such as Quora, Yahoo! Answers and the Stack Exchange family of forums. These websites provide natural labelled data allowing us to apply machine learning techniques. Most previous state-of-the-art approaches to the tasks of CQA-based question answering involved handcrafted features in combination with linear models. In this thesis we hypothesise that the use of handcrafted features can be avoided and the tasks can be approached with representation learning techniques, specifically deep learning. In the first part of this thesis we give an overview of deep learning in natural language processing and empirically evaluate our hypothesis on the task of detecting semantically equivalent questions, i.e. predicting if two questions can be answered by the same answer. In the second part of the thesis we address the task of answer ranking, i.e. determining how suitable an answer is for a given question. In order to determine the suitability of representation learning for the task of answer ranking, we provide a rigorous experimental evaluation of various neural architectures, based on feedforward, recurrent and convolutional neural networks, as well as their combinations. This thesis shows that deep learning is a very suitable approach to CQA-based QA, achieving state-of-the-art results on the two tasks we addressed

A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark Datasets

Machine Reading Comprehension (MRC) is a challenging NLP research field with wide real world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed the human performance on many datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need of improving existing datasets, evaluation metrics and models to move the MRC models toward 'real' understanding. To address this lack of comprehensive survey of existing MRC tasks, evaluation metrics and datasets, herein, (1) we analyzed 57 MRC tasks and datasets; proposed a more precise classification method of MRC tasks with 4 different attributes (2) we summarized 9 evaluation metrics of MRC tasks and (3) 7 attributes and 10 characteristics of MRC datasets; (4) We also discussed some open issues in MRC research and highlight some future research directions. In addition, to help the community, we have collected, organized, and published our data on a companion website(https://mrc-datasets.github.io/) where MRC researchers could directly access each MRC dataset, papers, baseline projects and browse the leaderboard.Comment: 59 page

Personalized Memory Transfer for Conversational Recommendation Systems

Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach