Open Access
Subscription Access
Open Access
Subscription Access
Deep Learning Approaches for Answer Selection in Question Answering System for Conversation Agents
Subscribe/Renew Journal
The conversation agent acts as core interfaces between a system and user in answering users queries with proper responses. Question answering system acquires an important role in the information retrieval field. The deep learning approach enhances the accuracy in answering complex questions. As outcome, the user is receiving the precise answer instead of large document collections. The aim of this paper is to develop a model with deep learning approach for improving answer selection process which supports more relevant answer displaying by conversation agents. To achieve this, word2vector used for word representation and biLSTM attentive model is used for training, testing and disclosure play precise answer. Question type is identified using POS-tagger based Question Pattern analysis (T-QPA) model. The knowledgebase is created from the bench mark datasets bAbI Facebook (simple QA tasks), TREC QA, Yahoo! Answer, Insurance QA dataset. The proposed framework is built by embedding of questions and answers based on bidirectional long short-term memory (biLSTM) attentive models. The similarity between questions and answers has been measured by semantic and cosine similarity. The proposed model reduces the search gap in extracting among user queries and answer sentences in the education domain. The system results are evaluated with the standard metrics MAP, Top 1 accuracy, F1- Score for the answer selection.
Keywords
Deep Learning, Question Answering System, Attentive Model, Conversation Agents, Cosine Similarity.
Subscription
Login to verify subscription
User
Font Size
Information
- Yashvardhan Sharma and Sahil Guptaa, “Deep Learning Approaches for Question Answering System”, Proceedings of International Conference on Computational Intelligence and Data Science, Vol. 132, pp. 785-794, 2018.
- James O. Shea and Zuhair Bandar Keeley, “Systems Engineering and Conversation Agents”, Master Thesis, School of Computing, Manchester Metropolitan University, pp. 1-232, 2011.
- G.O. Sing, K.W. Wong, C.C. Fung and A. Depickere, “Towards a more Natural and Intelligent Interface with Embodied Conversation Agent”, Proceedings of International Conference on Game Research and Development, pp. 177-183, 2006.
- Chatbots, “Alice (Artificial Linguistic Internet Computer Entity)”, Available at: chatbots.org/chatbot/a.l.i.c.e/
- M. Tan, C.Dos Santos, B. Xiang and B. Zhou, “Improved Representation Learning for Question Answer Matching”, Proceedings of 54th Annual Meeting of the Association for Computational Linguistics, pp. 464-473, 2016.
- X. Li and D. Roth, “Learning Question Classifiers”, Proceedings of 19th International Conference on Computational Linguistics, pp. 1-7, 2002.
- The Babi Project, Available at: https://research.fb.com/downloads/babi/
- Antoine Bordes, Nicolas Usunier, Sumit Chopra and Jason Weston, “Large-Scale Simple Question Answering with Memory Networks”, Proceedings of International Conference on Computation and Language, pp. 1-10, 2015.
- Analytics Vidhya, “Essentials of Deep Learning: Introduction to Long Short Term Memory”, Available at: https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/
- M. Feng, B. Xiang, M.R. Glass, L. Wang and B. Zhou, “Applying Deep Learning to Answer Selection: A Study and An Open Task”, Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 1-10, 2015.
- T.L. Lai, T. Bui and S. Li, “A Review on Deep Learning Techniques Applied to Answer Selection”, Proceedings of 27th International Conference on Computational Linguistics, pp. 2132-2144, 2018.
- Ming Tan, Bing Xiang and Bowen Zhou, “LSTM-based Deep Learning Models for Non-Factoid Answer Selection”, Proceedings of International Conference on Computational Language and Machine Learning, pp. 1-7, 2016.
- Tom Young, Devamanyu Hazarika, Soujanya Poria and Erik Cambria, “Recent Trends in Deep Learning based Natural Language Processing”, IEEE Computational Intelligence Magazine, Vol. 13, No. 3, pp. 55-75, 2017.
- Lei Yu, Karl Moritz Hermann, Phil Blunsom and Stephen G. Pulman, “Deep Learning for Answer Sentence Selection”, Proceedings of International Workshop on Deep Learning, pp. 1-9, 2014.
- K. Karpagam and A. Saradha, “A Framework For Intelligent Question Answering System using Semantic Context Specific Document Clustering and Wordnet”, Sadhana-Academy Proceedings in Engineering Sciences, Vol. 44, No. 3, pp. 1-10, 2019.
- K. O. Shea, “An Approach to Conversational Agent Design using Semantic Sentence Similarity”, Applied Intelligence, Vol. 37, No. 4, pp. 558-568, 2012.
- Ming-Wei Chang, Lev Ratinov, Dan Roth and Vivek Srikumar, “Importance of Semantic Representation: Data less Classification”, Proceedings of 23rd AAAI Conference on Artificial Intelligence, pp. 830-835, 2008.
- Ellen M Voorhees, “The TREC Question Answering Track”, Journal of Natural Language Engineering, Vol. 7, No. 4, pp. 361-378, 2001.
Abstract Views: 216
PDF Views: 0