QA Systems and Deep Learning Technologies — Part 1

Image for post
Image for post

1. Introduction

The automatic question and answering (QA) system has been in use for decades now. However, Siri’s and Watson’s success in 2011 has captured the whole industry’s attention. Since the success of these two technologies, the automatic QA system has stepped further into the limelight as a standalone practical application.

2. Overview of QA Systems

QA systems can interpret a user’s questions described in natural language and return concise and accurate matched answers by searching the heterogeneous corpora or, in more common terms, the QA knowledge bases. Compared with search engines, QA systems can better interpret the intended meaning of the user’s questions and therefore can meet the user’s information requirements more efficiently.

2.1 History of QA Systems

The Turing test is the earliest example of a QA system implementation and tests a machine’s ability for human intelligence. The Turing test requires the computer to answer a series of questions asked by human testers within 5 minutes. With the development of relevant technologies, such as artificial intelligence (AI) and natural language processing, different QA systems use various data types. Due to the limitation of intelligent technologies and domain data scales, early QA systems were mainly restricted to AI systems or expert systems of a limited domain, such as STUDENT [1] and LUNAR [2] systems. During this period, QA systems processed structured data. The system would translate the input questions into database query statements and then implement database retrieval and provide the feedback. With the rapid development of the internet and the rise of natural language processing technology, QA systems entered the open-domain-oriented and free-text-data-based development stage, such as the English QA retrieval systems Ask Jeeves (http://www.ask.com) and START (http://start.csail.mit.edu). The processing flow of such QA systems mainly includes question analysis, document and paragraph retrieval, candidate answer extraction, and answer validation. The introduction of the Question Answering Track (QA Track) at the Text Retrieval Conference (TREC) in 1999 promoted research and development based on natural language processing technology in the QA field.

2.2 Processing Framework of QA Systems

Different QA systems are subject to different data processing methods. For example,
● FAQ-oriented QA system: obtains the candidate’s answers directly from question retrieval.
● Open-domain-oriented QA systems: implement retrieval of information about relevant documents and text segments first according to the question analysis results, and then extract the candidate answers.

Image for post
Image for post

2.2.1 Question Interpretation

Question interpretation is a crucial link to interpret the user’s intentions in the QA system, and the question interpretation module performance directly restricts the effect of the subsequent processing module. The user’s intention is an abstract concept translated into a form that the machine can interpret and use as the basis of answer retrieval. The user’s retrieval intentions lead to the generation of information requirements and are a representation of the user’s intentions in research. According to the semantic structure, questions can be represented in two ways — class and content. The natural language processing technology is used for deep interpretation of the questions, including named entity recognition, dependency parsing, and word sense disambiguation.

2.2.2 Information Retrieval

According to the queries obtained from question interpretation, the information retrieval module retrieves relevant information from heterogeneous corpora and QA knowledge bases, and then transfers it to the answer generation processing module. For different QA systems, the retrieval model and retrieval data forms of the system are also different. For free-text-data-based QA systems, information retrieval is a filtering process to gradually narrow the scope of answers, including the retrieval of documents and paragraph sentence groups. For QA-pair-based QA systems, the information retrieval is to obtain the candidate questions similar to the user’s question by question retrieval and return the corresponding candidate answer list.

Information Retrieval Step 1: Document Retrieval

First, document retrieval retrieves the document sets related to the user’s question according to the question interpretation results. The simplest method is to implement full-text indexing of non-stop words in the question directly using the existing retrieval system (e.g., Smart and Lucene), and retrieve directly to obtain the document sets related to the user’s question. The document retrieval models in QA systems include the Boolean model, vector space model, language model, and probability model.

Information Retrieval Step 2: Paragraph Sentence Retrieval

The paragraph sentence group retrieval refers to retrieving the paragraphs (natural paragraphs or document fragments) that contain the answers from the candidate document sets, and further filtering the noise information to get a more accurate answer. There are three widely used paragraph retrieval algorithms: MultiText algorithm [6], IBM’s algorithm [7, 8] and SiteQ algorithm [9]. The experimental results of Tellex [10] et al. show that the density-based algorithm can obtain relatively good results. The so-called density-based algorithm determines the correlation of the paragraph according to the occurrence times and proximity of the keywords within it. The retrieval algorithm proposed by Hang Cui, by contrast, parses both the question and answer into syntactic trees and expose a correlation from the structure of the syntactic trees.

Information Retrieval Step 3: Question Retrieval

The main problem for question retrieval is how to narrow the semantic gap between the user’s question and the question in the knowledge base. In recent years, researchers used a method based on the rolling over model to calculate the translation probability to “translate” the user’s question into the retrieval question, so as to implement question retrieval by similarity. For example, in the algorithms [11–14], the two questions are regarded as statements represented in different ways to calculate the translation probability between them. To calculate the translation probability, the probability of translation between words should be estimated. This method requires obtaining a synonymous or near-synonymous QA pair set first by calculating the similarity of answers, and then estimate the translation probability. Experiments show that this model can achieve better results than the language model, Okapi BM25, and vector space model.

2.2.3 Answer Generation

Based on the retrieval information, the answer generation module implements candidate answer extraction and answer confidence calculation, and finally, returns a concise and correct answer. According to data granularity of the answers, the candidate answer extraction is classified into paragraph answer extraction, sentence answer extraction, and lexical phrase answer extraction.

3. Conclusion

With the rapid development of the Internet, the vast amount of available information is growing exponentially. The traditional search engines do help users to search information conveniently to some extent, but the retrieved information is a mix of relevant and irrelevant results. It is this growing need for a more intelligent information retrieval system that Question-Answering system caters.

4. References

[1] Terry Winograd. Five Lectures on Artificial Intelligence [J]. Linguistic Structures Processing, volume 5 of Fundamental Studies in Computer Science, pages 399- 520, North Holland, 1977.
[2] Woods W A. Lunar rocks in natural English: explorations in natural language question answering [J]. Linguistic Structures Processing, 1977, 5: 521−569.
[3] Dell Zhang and Wee Sun Lee. Question classification using support vector machines. In SIGIR, pages 26–32. ACM, 2003
[4] Xin Li and Dan Roth. Learning question classifiers. In COLING, 2002
[5] Hang Cui, Min-Yen Kan, and Tat-Seng Chua. Unsupervised learning of soft patterns for generating definitions from online news. In Stuart I. Feldman, Mike Uretsky, Marc Najork, and Craig E. Wills, editors, Proceedings of the 13th international conference on World Wide Web, WWW 2004, New York, NY, USA, May 17–20, 2004, pages 90–99. ACM, 2004.
[6] Clarke C, Cormack G, Kisman D, et al. Question answering by passage selection (multitext experiments for TREC-9) [C]//Proceedings of the 9th Text Retrieval Conference(TREC-9), 2000.
[7] Ittycheriah A, Franz M, Zhu W-J, et al. IBM’s statistical question answering system[C]//Proceedings of the 9th Text Retrieval Conference (TREC-9), 2000.
[8] Ittycheriah A, Franz M, Roukos S. IBM’s statistical question answering system — TREC-10[C]//Proceedings of the 10th Text Retrieval Conference (TREC 2001), 2001.
[9] Lee G, Seo J, Lee S, et al. SiteQ: engineering high performance QA system using lexico-semantic pattern.
[10] Tellex S, Katz B, Lin J, et al. Quantitative evaluation of passage retrieval algorithms for question answering[C]// Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘03). New York, NY, USA: ACM, 2003:41–47.
[11] Jiwoon Jeon, W. Bruce Croft, and Joon Ho Lee. Finding similar questions in large question and answer archives. In Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management, Bremen, Germany, October 31 — November 5, 2005, pages 84–90. ACM, 2005.
[12] S. Riezler, A. Vasserman, I. Tsochantaridis, V. Mittal, Y. Liu, Statistical machine translation for query expansion in answer retrieval, in: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Association for Computational Linguistics, Prague, Czech Republic, 2007, pp. 464–471.
[13] M. Surdeanu, M. Ciaramita, H. Zaragoza, Learning to rank answers on large online qa collections, in: ACL, The Association for Computer Linguistics, 2008, pp. 719–727.
[14] A. Berger, R. Caruana, D. Cohn, D. Freitag, V. Mittal, Bridging the lexical chasm: statistical approaches to answer-finding, in: SIGIR ’00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, ACM, New York, NY, USA, 2000, pp. 192–199.
[15] Gondek, D. C., et al. “A framework for merging and ranking of answers in DeepQA.” IBM Journal of Research and Development 56.3.4 (2012): 14–1.
[16] Wang, Chang, et al. “Relation extraction and scoring in DeepQA.” IBM Journal of Research and Development 56.3.4 (2012): 9–1.
[17] Kenneth C. Litkowski. Question-Answering Using Semantic Triples[C]. Eighth Text Retrieval Conference (TREC-8). Gaithersburg, MD. November 17–19, 1999.
[18] H. Cui, R. Sun, K. Li, M.-Y. Kan, T.-S. Chua, Question answering passage retrieval using dependency relations., in: R. A. Baeza-Yates, N. Ziviani, G. Marchionini, A. Moffat, J. Tait (Eds.), SIGIR, ACM, 2005, pp. 400–407.
[19] M. Wang, N. A. Smith, T. Mitamura, What is the jeopardy model? a quasisynchronous grammar for qa., in: J. Eisner (Ed.), EMNLP-CoNLL, The Association for Computer Linguistics, 2007, pp. 22–32.
[20] K. Wang, Z. Ming, T.-S. Chua, A syntactic tree matching approach to finding similar questions in community-based qa services, in: Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’09, 2009, pp. 187–194.
[21] Hovy, E.H., U. Hermjakob, and Chin-Yew Lin. 2001. The Use of External Knowledge of Factoid QA. In Proceedings of the 10th Text Retrieval Conference (TREC 2001) [C], Gaithersburg, MD, U.S.A., November 13–16, 2001.
[22] Jongwoo Ko, Laurie Hiyakumoto, Eric Nyberg. Exploiting Multiple Semantic Resources for Answer Selection. In Proceedings of LREC(Vol. 2006).
[23] Kasneci G, Suchanek F M, Ifrim G, et al. Naga: Searching and ranking knowledge. IEEE, 2008:953–962.
[24] Zhang D, Lee W S. Question Classification Using Support Vector Machines[C]. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2003. New York, NY, USA: ACM, SIGIR’03.
[25] X. Yao, B. V. Durme, C. Callison-Burch, P. Clark, Answer extraction as sequence tagging with tree edit distance., in: HLT-NAACL, The Association for Computer Linguistics, 2013, pp. 858–867.
[26] C. Shah, J. Pomerantz, Evaluating and predicting answer quality in community qa, in: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’10, 2010, pp. 411–418.
[27] T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, CoRR abs/1301.3781.
[28] Socher R, Lin C, Manning C, et al. Parsing natural scenes and natural language with recursive neural networks[C]. Proceedings of International Conference on Machine Learning. Haifa, Israel: Omnipress, 2011: 129–136.
[29] A. Graves, Generating sequences with recurrent neural networks, CoRR abs/1308.0850.
[30] Kalchbrenner N, Grefenstette E, Blunsom P. A Convolutional Neural Network for Modelling Sentences[C]. Proceedings of ACL. Baltimore and USA: Association for Computational Linguistics, 2014: 655–665.
[31] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate [J]. arXiv, 2014.
[32] Sutskever I, Vinyals O, Le Q V. Sequence to Sequence Learning with Neural Networks[M]. Advances in Neural Information Processing Systems 27. 2014: 3104–3112.
[33] Socher R, Pennington J, Huang E H, et al. Semi-supervised recursive auto encoders for predicting sentiment distributions[C]. EMNLP 2011
[34] Tang D, Wei F, Yang N, et al. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification[C]. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Baltimore, Maryland: Association for Computational Linguistics, 2014: 1555–1565.
[35] Li J, Luong M T, Jurafsky D. A Hierarchical Neural Autoencoder for Paragraphs and Documents[C]. Proceedings of ACL. 2015.
[36] Kim Y. Convolutional Neural Networks for Sentence Classification[C]. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics, 2014: 1746–1751.
[37] Zeng D, Liu K, Lai S, et al. Relation Classification via Convolutional Deep Neural Network[C]. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. Dublin, Ireland: Association for Computational Linguistics, 2014: 2335–2344.
[38] L. Yu, K. M. Hermann, P. Blunsom, and S. Pulman. Deep learning for answer sentence selection. CoRR, 2014.
[39] B. Hu, Z. Lu, H. Li, Q. Chen, Convolutional neural network architectures for matching natural language sentences., in: Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (Eds.), NIPS, 2014, pp. 2042–2050.
[40] A. Severyn, A. Moschitti, Learning to rank short text pairs with convolutional deep neural networks., in: R. A. Baeza-Yates, M. Lalmas, A. Moffat, B. A. Ribeiro-Neto (Eds.), SIGIR, ACM, 2015, pp. 373–382.
[41] Wen-tau Yih, Xiaodong He, and Christopher Meek. 2014. Semantic parsing for single-relation question answering. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 643–648. Association for Computational Linguistics.
[42] Li Dong, Furu Wei, Ming Zhou, and Ke Xu. 2015. Question Answering over Freebase with Multi-Column Convolutional Neural Networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL) and the 7th International Joint Conference on Natural Language Processing.
[43] Hochreiter S, Bengio Y, Frasconi P, et al. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies[M]. A Field Guide to Dynamical Recurrent Neural Networks. New York, NY, USA: IEEE Press, 2001.
[44] Hochreiter S, Schmidhuber J. Long Short-Term Memory[J]. Neural Comput., 1997, 9(8): 1735–1780.
[45] Graves A. Generating Sequences With Recurrent Neural Networks[J]. CoRR, 2013, abs/1308.0850.
[46] Chung J, Gülçehre Ç, Cho K, et al. Gated Feedback Recurrent Neural Networks[C]. Proceedings of the 32nd International Conference on Machine Learning (ICML-15). Lille, France: JMLR Workshop and Conference Proceedings, 2015: 2067–2075.
[47] D.Wang, E. Nyberg, A long short-term memory model for answer sentence selection in question answering, in: ACL, The Association for Computer Linguistics, 2015, pp. 707–712.
[48] Malinowski M, Rohrbach M, Fritz M. Ask your neurons: A neural-based approach to answering questions about images[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1–9.
[49] Gao H, Mao J, Zhou J, et al. Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question[C]//Advances in Neural Information Processing Systems. 2015: 2287–2295.
[50] Sun M S. Natural Language Processing Based on Naturally Annotated Web Resources [J]. Journal of Chinese Information Processing, 2011, 25(6): 26–32.
[51] Hu B, Chen Q, Zhu F. LCSTS: a large scale chinese short text summarization dataset[J]. arXiv preprint arXiv:1506.05865, 2015.
[52] Shang L, Lu Z, Li H. Neural Responding Machine for Short-Text Conversation[C]. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, China: Association for Computational Linguistics, 2015: 1577–1586.
[53] O. Vinyals, and Q. V. Le. A Neural Conversational Model. arXiv: 1506.05869,2015.
[54] Kumar A, Irsoy O, Su J, et al. Ask me anything: Dynamic memory networks for natural language processing[J]. arXiv preprint arXiv:1506.07285, 2015.
[55] Sukhbaatar S, Weston J, Fergus R. End-to-end memory networks[C]//Advances in Neural Information Processing Systems. 2015: 2431–2439.

Written by

Follow me to keep abreast with the latest technology news, industry insights, and developer trends.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store