skip to main content
research-article

A Deep Neural Network Framework for English Hindi Question Answering

Authors Info & Claims
Published:21 November 2019Publication History
Skip Abstract Section

Abstract

In this article, we propose a unified deep neural network framework for multilingual question answering (QA). The proposed network deals with the multilingual questions and answers snippets. The input to the network is a pair of factoid question and snippet in the multilingual environment (English and Hindi), and output is the relevant answer from the snippet. We begin by generating the snippet using a graph-based language-independent algorithm, which exploits the lexico-semantic similarity between the sentences. The soft alignment of the question words from the English and Hindi languages has been used to learn the shared representation of the question. The learned shared representation of question and attention-based snippet representation are passed as an input to the answer extraction layer of the network, which extracts the answer span from the snippet. Evaluation on a standard multilingual QA dataset shows the state-of-the-art performance with 39.44 Exact Match (EM) and 44.97 F1 values. Similarly, we achieve the performance of 50.11 Exact Match (EM) and 53.77 F1 values on Translated SQuAD dataset.

References

  1. Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu. 2013. Deep canonical correlation analysis. In Proceedings of the International Conference on Machine Learning. 1247--1255.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Sarath Chandar A. P., Stanislas Lauly, Hugo Larochelle, Mitesh Khapra, Balaraman Ravindran, Vikas C. Raykar, and Amrita Saha. 2014. An autoencoder approach to learning bilingual word representations. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 1853--1861.Google ScholarGoogle Scholar
  3. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Trans. Assoc. Comput. Ling. 5 (2017), 135--146. DOI:https://doi.org/10.1162/tacl_a_00051.Google ScholarGoogle ScholarCross RefCross Ref
  4. Antoine Bordes, Sumit Chopra, and Jason Weston. 2014. Question answering with subgraph embeddings. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). Association for Computational Linguistics, 615--620. Retrieved from: http://www.aclweb.org/anthology/D14-1067.Google ScholarGoogle ScholarCross RefCross Ref
  5. Mitchell Bowden, Marian Olteanu, Pasin Suriyentrakorn, Thomas d’Silva, and Dan Moldovan. 2007. Multilingual question answering through intermediate translation: LCC’s PowerAnswer at QA@ CLEF 2007. In Proceedings of the Workshop of the Cross-Language Evaluation Forum for European Languages. Springer, 273--283.Google ScholarGoogle Scholar
  6. Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In Proceedings of the NIPS Workshop on Deep Learning.Google ScholarGoogle Scholar
  7. Arpita Das, Harish Yenala, Manoj Chinnakotla, and Manish Shrivastava. 2016. Together we stand: Siamese networks for similar question retrieval. In Proceedings of the 54th Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 378--387. DOI:https://doi.org/10.18653/v1/P16-1036Google ScholarGoogle ScholarCross RefCross Ref
  8. Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum. 2019. Multi-step retriever-reader interaction for scalable open-domain question answering. In Proceedings of the International Conference on Learning Representations. Retrieved from: https://openreview.net/forum?id=HkfPSh05K7.Google ScholarGoogle Scholar
  9. Asif Ekbal, Deepak Gupta, Surabhi Kumari, and Pushpak Bhattacharyya. 2018. MMQA: A multi-domain multi-lingual question-answering framework for English and Hindi. In Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC’18) (7-12). European Language Resources Association (ELRA).Google ScholarGoogle Scholar
  10. Pamela Forner, Anselmo Peñas, Eneko Agirre, Iñaki Alegria, Corina Forăscu, Nicolas Moreau, Petya Osenova, Prokopis Prokopidis, Paulo Rocha, Bogdan Sacaleanu et al. 2008. Overview of the CLEF 2008 multilingual question answering track. In Proceedings of the Workshop of the Cross-Language Evaluation Forum for European Languages. Springer, 262--295.Google ScholarGoogle Scholar
  11. Jerome H. Friedman. 2001. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 5 (2001), 1189--1232.Google ScholarGoogle ScholarCross RefCross Ref
  12. María Dolores García Santiago and María Dolores Olvera-Lobo. 2010. Automatic web translators as part of a multilingual question-answering (QA) system: Translation of questions. Transl. J. 14, 1 (2010).Google ScholarGoogle Scholar
  13. Danilo Giampiccolo, Pamela Forner, Jesús Herrera, Anselmo Peñas, Christelle Ayache, Corina Forascu, Valentin Jijkoun, Petya Osenova, Paulo Rocha, Bogdan Sacaleanu et al. 2007. Overview of the CLEF 2007 multilingual question answering track. In Proceedings of the Workshop of the Cross-Language Evaluation Forum for European Languages. Springer, 200--236.Google ScholarGoogle Scholar
  14. Deepak Gupta, Pabitra Lenka, Asif Ekbal, and Pushpak Bhattacharyya. 2018a. Uncovering code-mixed challenges: A framework for linguistically driven question generation and neural-based question answering. In Proceedings of the 22nd Conference on Computational Natural Language Learning. Association for Computational Linguistics, 119--130. DOI:https://doi.org/10.18653/v1/K18-1012Google ScholarGoogle ScholarCross RefCross Ref
  15. Deepak Gupta, Rajkumar Pujari, Asif Ekbal, Pushpak Bhattacharyya, Anutosh Maitra, Tom Jain, and Shubhashis Sengupta. 2018. Can taxonomy help? Improving semantic question matching using question taxonomy. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, , 499--513. Retrieved from: https://www.aclweb.org/anthology/C18-1042.Google ScholarGoogle Scholar
  16. David R. Hardoon, Sandor Szedmak, and John Shawe-Taylor. 2004. Canonical correlation analysis: An overview with application to learning methods. Neural Comput. 16, 12 (2004), 2639--2664.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Xiaodong He and David Golub. 2016. Character-level question answering with attention. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 1598--1607. Retrieved from: https://aclweb.org/anthology/D16-1166.Google ScholarGoogle ScholarCross RefCross Ref
  18. Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, and Ming Zhou. 2018. Reinforced mnemonic reader for machine reading comprehension. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). Retrieved from: http://www.ijcai.org/proceedings/2018/.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Shafiq Joty, Preslav Nakov, Lluís Màrquez, and Israa Jaradat. 2017. Cross-language learning with adversarial neural networks. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL’17). 226--237.Google ScholarGoogle ScholarCross RefCross Ref
  20. Praveen Kumar, Shrikant Kashyap, Ankush Mittal, and Sumit Gupta. 2005. A Hindi question answering system for E-learning documents. In Proceedings of the 3rd International Conference on Intelligent Sensing and Information Processing (ICISIP’05). IEEE, 80--85.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Xin Li and Dan Roth. 2002. Learning question classifiers. In Proceedings of the 19th International Conference on Computational Linguistics, (COLING’02). Association for Computational Linguistics, 1--7.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Anutosh Maitra, Shubhashis Sengupta, Abhisek Mukhopadhyay, Deepak Gupta, Rajkumar Pujari, Pushpak Bhattacharya, Asif Ekbal, and Tom Geo Jain. 2018. Semantic question matching in data constrained environment. In Text, Speech, and Dialogue, Petr Sojka, Aleš Horák, Ivan Kopeček, and Karel Pala (Eds.). Springer International Publishing, Cham, 267--276.Google ScholarGoogle Scholar
  23. Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Meeting of the Association for Computational Linguistics: System Demonstrations. 55--60.Google ScholarGoogle ScholarCross RefCross Ref
  24. Bernardo Magnini Matteo, Matteo Negri, Roberto Prevete, and Hristo Tanev. 2001. Multilingual question/answering: The DIOGENE system. In Proceedings of the 10th Text Retrieval Conference.Google ScholarGoogle Scholar
  25. Donald Metzler and W. Bruce Croft. 2007. Linear feature-based models for information retrieval. Inf. Retr. 10, 3 (June 2007), 257--274. DOI:https://doi.org/10.1007/s10791-006-9019-zGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  26. María-Dolores Olvera-Lobo and Juncal Gutiérrez-Artacho. 2011. Multilingual Question-Answering System in Biomedical Domain on the Web: An Evaluation. Springer Berlin, 83--88. DOI:https://doi.org/10.1007/978-3-642-23708-9_10Google ScholarGoogle Scholar
  27. Jahna Otterbacher, Gunes Erkan, and Dragomir R. Radev. 2009. Biased LexRank: Passage retrieval using random walks with question-based priors. Inform. Proc. Manag. 45, 1 (2009), 42--54.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the 40th Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 311--318.Google ScholarGoogle Scholar
  29. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2383--2392. DOI:https://doi.org/10.18653/v1/D16-1264Google ScholarGoogle ScholarCross RefCross Ref
  30. Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomas Kocisky, and Phil Blunsom. 2016. Reasoning about entailment with neural attention. In Proceedings of the International Conference on Learning Representations (ICLR’16).Google ScholarGoogle Scholar
  31. Shriya Sahu, Nandkishor Vasnik, and Devshri Roy. 2012. Prashnottar: A Hindi question answering system. Int. J. Comput. Sci. Inform. Technol. 4, 2 (2012), 149.Google ScholarGoogle ScholarCross RefCross Ref
  32. Satoshi Sekine and Ralph Grishman. 2003. Hindi-English cross-lingual question-answering system. ACM Tran. Asian Lang. Inform. Proc. 2, 3 (2003), 181--192.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hananneh Hajishirzi. 2017. Bidirectional attention flow for machine comprehension. In Proceedings of the International Conference on Learning Representations.Google ScholarGoogle Scholar
  34. Samuel L. Smith, David H. P. Turban, Steven Hamblin, and Nils Y. Hammerla. 2017. Offline bilingual word vectors, orthogonal transformations, and the inverted softmax. In Proceedings of the International Conference on Learning Representations.Google ScholarGoogle Scholar
  35. Daniil Sorokin and Iryna Gurevych. 2017. End-to-end representation learning for question answering with weak supervision. In Proceedings of the Semantic Web Evaluation Challenge. Springer, 70--83.Google ScholarGoogle ScholarCross RefCross Ref
  36. Shalini Stalin, Rajeev Pandey, and Raju Barskar. 2012. Web based application for Hindi question answering system. Int. J. Electron. Comput. Sci. Eng. 2, 1 (2012), 72--78.Google ScholarGoogle Scholar
  37. Sai Praneeth Suggu, Kushwanth Naga Goutham, Manoj K. Chinnakotla, and Manish Shrivastava. 2016. Hand in glove: Deep feature fusion network architectures for answer quality prediction in community question answering. In Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. The COLING 2016 Organizing Committee, 1429--1440. Retrieved from: https://www.aclweb.org/anthology/C16-1135.Google ScholarGoogle Scholar
  38. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the Conference on Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 3104--3112. Retrieved from: http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf.Google ScholarGoogle Scholar
  39. Chuanqi Tan, Furu Wei, Nan Yang, Bowen Du, Weifeng Lv, and Ming Zhou. 2018. S-Net: From answer extraction to answer synthesis for machine reading comprehension. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’18).Google ScholarGoogle Scholar
  40. Ming Tan, Cicero dos Santos, Bing Xiang, and Bowen Zhou. 2015. LSTM-based deep learning models for non-factoid answer selection. Retrieved from: arXiv preprint arXiv:1511.04108 (2015).Google ScholarGoogle Scholar
  41. Ferhan Ture and Oliver Jojic. 2017. No need to pay attention: Simple recurrent neural networks work! In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2866--2872. Retrieved from: https://www.aclweb.org/anthology/D17-1307.Google ScholarGoogle Scholar
  42. Kateryna Tymoshenko and Alessandro Moschitti. 2015. Assessing the impact of syntactic and semantic structures for answer passages reranking. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 1451--1460.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Proceedings of the Conference on Advances in Neural Information Processing Systems 28, C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett (Eds.). Curran Associates, Inc., 2692--2700. Retrieved from: http://papers.nips.cc/paper/5866-pointer-networks.pdf.Google ScholarGoogle Scholar
  44. Shuohang Wang and Jing Jiang. 2017. Machine comprehension using match-LSTM and answer pointer. In Proceedings of the International Conference on Learning Representations (ICLR’17).Google ScholarGoogle Scholar
  45. Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017. Gated self-matching networks for reading comprehension and question answering. In Proceedings of the 55th Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 189--198.Google ScholarGoogle ScholarCross RefCross Ref
  46. Qiang Wu, Christopher J. C. Burges, Krysta M. Svore, and Jianfeng Gao. 2010. Adapting boosting for information retrieval measures. Inform. Retr. 13, 3 (2010), 254--270.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Liu Yang, Qingyao Ai, Jiafeng Guo, and W. Bruce Croft. 2016a. aNMM: Ranking short answer texts with attention-based neural matching model. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 287--296.Google ScholarGoogle Scholar
  48. Liu Yang, Qingyao Ai, Damiano Spina, Ruey-Cheng Chen, Liang Pang, W. Bruce Croft, Jiafeng Guo, and Falk Scholer. 2016b. Beyond factoid QA: Effective methods for non-factoid answer sentence retrieval. In Proceedings of the European Conference on Information Retrieval. Springer, 115--128.Google ScholarGoogle ScholarCross RefCross Ref
  49. Dawei Yin, Yuening Hu, Jiliang Tang, Tim Daly, Mianwei Zhou, Hua Ouyang, Jianhui Chen, Changsung Kang, Hongbo Deng, Chikashi Nobata, Jean-Marc Langlois, and Yi Chang. 2016. Ranking relevance in Yahoo search. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). ACM, New York, NY, 323--332. DOI:https://doi.org/10.1145/2939672.2939677.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Adams Wei Yu, David Dohan, Thang Luong, Rui Zhao, Kai Chen, and Quoc Le. 2018. QANet: Combining local convolution with global self-attention for reading comprehension. In Proceedings of the International Conference on Learning Representations (ICLR’18). Retrieved from: https://openreview.net/pdf?id=B14TlG-RW.Google ScholarGoogle Scholar
  51. Matthew D. Zeiler. 2012. ADADELTA: An adaptive learning rate method. Retrieved from: arXiv preprint arXiv:1212.5701 (2012).Google ScholarGoogle Scholar

Index Terms

  1. A Deep Neural Network Framework for English Hindi Question Answering

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!