© August 2018 | IJIRT | Volume 5 Issue 3 | ISSN: 2349-6002 IJIRT 147009 INTERNATIONAL JO URNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 54 Neural Intent Recognition for Question-Answering System Ajinkya Pradeep Indulkar 1 , Srivatsan Varadharajan 2 , Krishnamurthy Nayak 3 1, 3 Department of E&C, Manipal Institute of Technology, Manipal 2 Philips Lighting India Limited, Bangalore Abstract- Conversational Agents, commonly known as Chatbots, are a successful result of the collaboration of Natural Language Processing (NLP) and Deep Learning. Major Technology Giants like Google, Amazon, Microsoft, etc. are heavily invested in developing a sophisticated conversational agent which can pass the Turing Test. There are various methodologies which can be used to develop the various components of such an agent from scratch. This paper uses SQuAD, an open Question-Answering dataset, for developing an Intent Recognition System for any Question-Answering system. Inspired by Author-Topic Modelling, a Title-Topic Modelling technique is used in combination with various Deep Learning models to train the Intent Recognition System, achieving an accuracy of 88.36%. Index Terms - Conversational Agents, Deep Learning, Intent Recognition, Natural Language Processing, Question-Answering System, Title-Topic Modelling. 1. INTRODUCT ION The origin of conversational agents can be traced back to 1950 when Alan Turing, father of Computer Science, published his first paper [1]. His question, “Can Machines think?” ushered the world into the era of Artificial Intelligence (AI) which began a plethora of research to turn his question into reality. Numerous sub-fields of AI like Machine Learning, Computer Vision, and recently, Deep Learning have seen more researchers committing themselves to develop novel techniques in the respective fields. The development of conversational agents falls under the field of Natural Language Processing, which is an area of research concerned with the understanding of human (natural) languages by the machines. Similar to Artificial Intelligence, its origin can also be traced back to 1950s when Alan Turing proposed a criterion for intelligence, known as the “Turing Test ”. A machine passes the test when a human is unable to distinguish the machine‟s responses from that of a human‟s. ELIZA [2] was the first implementation of a human- like dialog system, developed in 1960s. It worked on a rule-based methodology. Since then, the number of research papers published in this field is overwhelming. NLP Techniques like Dependency Parsing, Named Entity Recognition, etc. are vital nowadays in the development of the most sophisticated conversational agent. A. Motivation The current scenario of the chatbot industry mainly involves detecting the intent of a user utterance to the conversational agent by extracting entities and using classic machine learning algorithms such as Support Vector Machines to develop a classifier for the pre- defined intents. This is big step from the previous, rule-based approach but still lacks the true AI capabilities. Natural Languages are complex, and a human can express an intent in more than one way. To be able to understand such complexities can allow any machine to pass the “Turing Test”. This paper explores methodologies involved in developing a Question-Answering system using the concepts from NLP and Deep Learning. The Neural Natural Language Understanding engine uses Intent Recognition System to understand the intents expressed by the user. For training the Intent Recognition System, Topic Modelling techniques are used to automatically label the training data, as the original dataset is unlabeled. The techniques used are modular, i.e. they can be used individually in other systems according to the researcher‟s requirement. II. LITERATURE REVIEW