Hierarchical context supplementation for consecutive question answering

Question-answering (QA) systems have recently shown impressive results in terms of accurately answering user questions in such situations as domain specific user questions. However, we have identified many real situations where QA systems must cope with not a single question-answering situation but rather a sequence of consecutive questions. In such cases, users often ask questions on the basis of the previous answer they have received, so the context of the questions changes on a certain level. The commonly used method to handle this problem when using a QA system is to append the current question to the previous question (append method). However, the append method is not designed to detect such context changes. To deal with such context changes, we have designed a hierarchical context supplementation QA System (HCSQ). The HCSQ handles consecutive questions by matching the current question with the hierarchical domain knowledge database structure of the previous answer and then supplements the context of the current question with the required keywords. We also show that our method can be further applied to the initial question to supplement omitted context. Experimental results show that our method substantially outperforms the state-of-the-art methods.

[1]  Grace Hui Yang,et al.  Structured use of external knowledge for event-based open domain question answering , 2003, SIGIR.

[2]  Erik T. Mueller,et al.  Watson: Beyond Jeopardy! , 2013, Artif. Intell..

[3]  Tunga Güngör,et al.  Question Analysis for a Closed Domain Question Answering System , 2015, CICLing.

[4]  Joyce Chai,et al.  Discourse Structure for Context Question Answering , 2004, HLT-NAACL 2004.

[5]  Paul-Alexandru Chirita,et al.  Personalized query expansion for the web , 2007, SIGIR.

[6]  William W. Cohen,et al.  Automatic Set Expansion for List Question Answering , 2008, EMNLP.

[7]  Ming Zhou,et al.  Answering Questions with Complex Semantic Constraints on Open Knowledge Bases , 2015, CIKM.

[8]  Yutaka Sasaki Question Answering as Abduction: A Feasibility Study at NTCIR QAC1 , 2003 .

[9]  Suresh Manandhar,et al.  Designing an interactive open-domain question answering system , 2009, Natural Language Engineering.

[10]  Natasa Milic-Frayling,et al.  Socializing or knowledge sharing?: characterizing social intent in community question answering , 2009, CIKM.

[11]  Sanda M. Harabagiu,et al.  Experiments with Interactive Question-Answering , 2005, ACL.

[12]  Kohtaroh Miyamoto,et al.  Continuous FAQ Updating for Service Incident Ticket Resolution , 2015 .

[13]  Hans Uszkoreit,et al.  Contextual phenomena and thematic relations in database QA dialogues: results from a Wizard-of-Oz Experiment , 2006, HLT-NAACL 2006.

[14]  Juhnyoung Lee,et al.  The Dialog manager a system for managing procedural knowledge , 2013, Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics.

[15]  Oren Etzioni,et al.  Open question answering over curated and extracted knowledge bases , 2014, KDD.

[16]  Juhnyoung Lee,et al.  Improving IT Service Incident Resolution by Using an FAQ System , 2014 .

[17]  Daniel Sonntag,et al.  Ontologies and Adaptivity in Dialogue for Question Answering , 2010, Studies on the Semantic Web.

[18]  Jignesh M. Patel,et al.  Estimating the selectivity of tf-idf based cosine similarity predicates , 2007, SGMD.

[19]  Tsuneaki Kato,et al.  Question Answering Challenge for Information Access Dialogue - Overview of NTCIR4 QAC2 Subtask , 2004, NTCIR.

[20]  Frédéric Béchet,et al.  DECODA: a call-centre human-human spoken conversation corpus , 2012, LREC.

[21]  Bonnie L. Webber,et al.  Discourse structure and language technology , 2011, Natural Language Engineering.

[22]  Jun Zhao,et al.  Relation Classification via Convolutional Deep Neural Network , 2014, COLING.

[23]  Toru Yamaguchi,et al.  Influence of approaching patterns of Telepresence Robot for personal space , 2015, 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI).