survey

Interactive Question Answering Systems: Literature Review

Authors:

Giovanni Maria Biancofiore,

Yashar Deldjoo,

Tommaso Di Noia,

Eugenio Di Sciascio,

Fedelucio NarducciAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 9

Article No.: 239, Pages 1 - 38

https://doi.org/10.1145/3657631

Published: 08 May 2024 Publication History

Abstract

Question-answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their queries by presenting their questions in natural language. Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems. On the one hand, the user can ask questions in normal language and locate the actual response to her inquiry; on the other hand, the system can prolong the question-answering session into a dialogue if there are multiple probable replies, very few, or ambiguities in the initial request. By permitting the user to ask more questions, interactive question answering enables users to interact with the system and receive more precise results dynamically.

This survey offers a detailed overview of the interactive question-answering methods that are prevalent in current literature. It begins by explaining the foundational principles of question-answering systems, hence defining new notations and taxonomies to combine all identified works inside a unified framework. The reviewed published work on interactive question-answering systems is then presented and examined in terms of its proposed methodology, evaluation approaches, and dataset/application domain. We also describe trends surrounding specific tasks and issues raised by the community, so shedding light on the future interests of scholars. Our work is further supported by a GitHub page synthesizing all the major topics covered in this literature study. https://sisinflab.github.io/interactive-question-answering-systems-survey/

References

[1]

Huda Alamri, Vincent Cartillier, Abhishek Das, Jue Wang, Anoop Cherian, Irfan Essa, Dhruv Batra, Tim K. Marks, Chiori Hori, Peter Anderson, and Stefan Lee, Devi Parikh. 2019. Audio visual scene-aware dialog. In CVPR. 7558–7567.

[2]

Kamran Alipour, Jürgen P. Schulze, Yi Yao, Avi Ziskind, and Giedrius Burachas. 2020. A study on multimodal and interactive explanations for visual question answering. In SafeAI@AAAI(CEUR Workshop Proceedings, Vol. 2560). CEUR-WS.org, 54–62.

[3]

Francesca Alloatti, Luigi Di Caro, and Gianpiero Sportelli. 2019. Real life application of a question answering system using BERT language model. In SIGdial. Association for Computational Linguistics, 250–253.

[4]

Ashton Anderson, Daniel Huttenlocher, Jon Kleinberg, and Jure Leskovec. 2012. Discovering value from community activity on focused question answering sites: A case study of stack overflow. In KDD. 850–858.

Digital Library

[5]

Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. 2015. VQA: Visual question answering. In ICCV. 2425–2433.

Digital Library

[6]

Ashutosh Baheti, Alan Ritter, and Kevin Small. 2020. Fluent response generation for conversational question answering. In ACL. Association for Computational Linguistics, 191–207.

[7]

Kinjal Basu. 2019. Conversational AI : Open domain question answering and commonsense reasoning. In ICLP Technical Communications(EPTCS, Vol. 306). 396–402.

[8]

Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic parsing on Freebase from question-answer pairs. In EMNLP. 1533–1544.

[9]

Ana Berdasco, Gustavo López, Ignacio Díaz-Oreiro, Luis Quesada, and Luis A. Guerrero. 2019. User experience comparison of intelligent personal assistants: Alexa, Google Assistant, Siri and Cortana. In UCAmI(MDPI Proceedings, Vol. 31). MDPI, 51.

[10]

Guillaume Le Berre and Philippe Langlais. 2020. Attending knowledge facts with BERT-like models in question-answering: Disappointing results and some explanations. In Canadian AI(Lecture Notes in Computer Science, Vol. 12109). Springer, 356–367.

Digital Library

[11]

Santanu Bhattacharjee, Rejwanul Haque, Gideon Maillette de Buy Wenniger, and Andy Way. 2020. Investigating query expansion and coreference resolution in question answering on BERT. In NLDB(Lecture Notes in Computer Science, Vol. 12089). Springer, 47–59.

[12]

Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: A collaboratively created graph database for structuring human knowledge. In SIGMOD. 1247–1250.

Digital Library

[13]

Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. 2015. Large-scale simple question answering with memory networks. CoRR abs/1506.02075 (2015).

[14]

Rakesh Chada. 2019. Gendered pronoun resolution using BERT and an extractive question answering formulation. CoRR abs/1906.03695 (2019).

[15]

Adriane Chapman and H. V. Jagadish. 2009. Why not? In SIGMOD. ACM, 523–534.

Digital Library

[16]

David L. Chen and William B. Dolan. 2011. Collecting highly parallel data for paraphrase evaluation. In ACL. The Association for Computer Linguistics, 190–200.

Digital Library

[17]

Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Wang, and William W. Cohen. 2020. Open question answering over tables and text. arXiv preprint arXiv:2010.10439 (2020).

[18]

Ting-Rui Chiang, Hao-Tong Ye, and Yun-Nung Chen. 2020. An empirical study of content understanding in conversational question answering. In AAAI. AAAI Press, 7578–7585.

[19]

Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An Open-source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. Retrieved from https://lmsys.org/blog/2023-03-30-vicuna/

[20]

Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018. QuAC: Question answering in context. arXiv preprint arXiv:1808.07036 (2018).

[21]

Philipp Christmann, Rishiraj Saha Roy, Abdalghani Abujabal, Jyotsna Singh, and Gerhard Weikum. 2019. Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion. In CIKM. ACM, 729–738.

Digital Library

[22]

Kevin Clark and Christopher D. Manning. 2016. Improving coreference resolution by learning entity-level distributed representations. In ACL.

[23]

Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. Think you have solved question answering? Try ARC, the AI2 reasoning challenge. arXiv preprint arXiv:1803.05457 (2018).

[24]

Daniel Cohen, Liu Yang, and W. Bruce Croft. 2018. WikiPassageQA: A benchmark collection for research on non-factoid answer passage retrieval. In SIGIR. 1165–1168.

Digital Library

[25]

Sérgio Curto, Ana Cristina Mendes, Pedro Curto, Luísa Coheur, and Ângela Costa. 2014. JUST.ASK, a QA system that learns to answer new questions from previous interactions. In LREC. European Language Resources Association (ELRA), 2603–2607.

[26]

Danica Damljanovic, Milan Agatonovic, and Hamish Cunningham. 2010. Natural language interfaces to ontologies: Combining syntactic analysis and ontology-based lookup through the user interaction. In ESWC(Lecture Notes in Computer Science, Vol. 6088). Springer, 106–120.

Digital Library

[27]

Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José M. F. Moura, Devi Parikh, and Dhruv Batra. 2017. Visual dialog. In CVPR. 326–335.

[28]

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum. 2019. Multi-step retriever-reader interaction for scalable open-domain question answering. In ICLR (Poster). OpenReview.net.

[29]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[30]

Bhuwan Dhingra, Kathryn Mazaitis, and William W. Cohen. 2017. Quasar: Datasets for question answering by search and reading. arXiv preprint arXiv:1707.03904 (2017).

[31]

Tuong Do, Huy Tran, Thanh-Toan Do, Erman Tjiputra, and Quang D. Tran. 2019. Compact trilinear interaction for visual question answering. In ICCV. IEEE, 392–401.

[32]

Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017. SearchQA: A newQ&A dataset augmented with context from a search engine. arXiv preprint arXiv:1704.05179 (2017).

[33]

Ronald Fagin, Amnon Lotem, and Moni Naor. 2003. Optimal aggregation algorithms for middleware. J. Comput. Syst. Sci. 66, 4 (2003), 614–656.

Digital Library

[34]

Minwei Feng, Bing Xiang, Michael R. Glass, Lidan Wang, and Bowen Zhou. 2015. Applying deep learning to answer selection: A study and an open task. In ASRU. 813–820.

[35]

Antonio Ferrández and Jesús Peral. 2010. The benefits of the interaction between data warehouses and question answering. In EDBT/ICDT Workshops (ACM International Conference Proceeding Series). ACM.

Digital Library

[36]

Jun-ichi Fukumoto, Noriaki Aburai, and Ryosuke Yamanishi. 2013. Interactive document expansion for answer extraction of question answering system. In KES(Procedia Computer Science, Vol. 22). Elsevier, 991–1000.

[37]

Peng Gao, Haoxuan You, Zhanpeng Zhang, Xiaogang Wang, and Hongsheng Li. 2019. Multi-modality latent interaction network for visual question answering. In ICCV. IEEE, 5824–5834.

[38]

Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, and Ali Farhadi. 2018. IQA: Visual question answering in interactive environments. In CVPR. Computer Vision Foundation/IEEE Computer Society, 4089–4098.

[39]

Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017. Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In CVPR. 6904–6913.

[40]

Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, and Jian Yin. 2018. Dialog-to-action: Conversational question answering over a large-scale knowledge base. In NeurIPS 2018. 2946–2955.

[41]

Wenya Guo, Ying Zhang, Jufeng Yang, and Xiaojie Yuan. 2021. Re-attention for visual question answering. IEEE Trans. Image Process. 30 (2021), 6730–6743.

Digital Library

[42]

Maryam Habibi, Parvaz Mahdabi, and Andrei Popescu-Belis. 2016. Question answering in conversations: Query refinement using contextual and semantic information. Data Knowl. Eng. 106 (2016), 38–51.

Digital Library

[43]

Hojae Han, Seungtaek Choi, Haeju Park, and Seung-won Hwang. 2019. MICRON: Multigranular interaction for contextualizing representation in non-factoid question answering. In EMNLP/IJCNLP. Association for Computational Linguistics, 5889–5894.

[44]

Sofian Hazrina, Nurfadhlina Mohd Sharef, Hamidah Ibrahim, Masrah Azrifah Azmi Murad, and Shahrul Azman Mohd. Noah. 2017. Review on the advancements of disambiguation in semantic question answering system. Inf. Process. Manag. 53, 1 (2017), 52–69.

[45]

Shizhu He, Kang Liu, and Weiting An. 2019. Learning to align question and answer utterances in customer service conversation with recurrent pointer networks. In AAAI. AAAI Press, 134–141.

Digital Library

[46]

Lynette Hirschman and Robert J. Gaizauskas. 2001. Natural language question answering: The view from here. Nat. Lang. Eng. 7, 4 (2001), 275–300.

Digital Library

[47]

Yining Hong, Jialu Wang, Yuting Jia, Weinan Zhang, and Xinbing Wang. 2019. Academic reader: An interactive question answering system on academic literatures. In AAAI. AAAI Press, 9855–9856.

Digital Library

[48]

Jun Hu, Shengsheng Qian, Quan Fang, and Changsheng Xu. 2018. Attentive interactive convolutional matching for community question answering in social multimedia. In ACM Multimedia. ACM, 456–464.

Digital Library

[49]

Hsin-Yuan Huang, Eunsol Choi, and Wen-tau Yih. 2018. FlowQA: Grasping flow in history for conversational machine comprehension. arXiv preprint arXiv:1810.06683 (2018).

[50]

Eric Hulburd. 2020. Exploring BERT parameter efficiency on the Stanford question answering dataset v2.0. CoRR abs/2002.10670 (2020).

[51]

Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. 2017. TGIF-QA: Toward spatio-temporal reasoning in visual question answering. In CVPR. IEEE Computer Society, 1359–1367.

[52]

Zhen Jia, Soumajit Pramanik, Rishiraj Saha Roy, and Gerhard Weikum. 2021. Complex temporal question answering on knowledge graphs. In CIKM. 792–802.

Digital Library

[53]

Weike Jin, Zhou Zhao, Mao Gu, Jun Yu, Jun Xiao, and Yueting Zhuang. 2019. Multi-interaction network with object relation for video question answering. In ACM Multimedia. ACM, 1193–1201.

Digital Library

[54]

Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551 (2017).

[55]

Ying Ju, Fubang Zhao, Shijie Chen, Bowen Zheng, Xuefeng Yang, and Yunfeng Liu. 2019. Technical report on Conversational Question Answering. CoRR abs/1909.10772 (2019).

[56]

Endri Kacupaj, Kuldeep Singh, Maria Maleshkova, and Jens Lehmann. 2022. Contrastive representation learning for conversational question answering over knowledge graphs. In CIKM. 925–934.

Digital Library

[57]

Kushal Kafle and Christopher Kanan. 2017. An analysis of visual question answering algorithms. In ICCV. 1965–1973.

[58]

Magdalena Kaiser, Rishiraj Saha Roy, and Gerhard Weikum. 2020. Conversational question answering over passages by leveraging word proximity networks. In SIGIR. ACM, 2129–2132.

Digital Library

[59]

Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020. QASC: A dataset for question answering via sentence composition. In AAAI. 8082–8090.

[60]

Kyung-Min Kim, Min-Oh Heo, Seong-Ho Choi, and Byoung-Tak Zhang. 2017. DeepStory: Video story QA by deep embedded memory networks. arXiv preprint arXiv:1707.00836 (2017).

[61]

Natalia Konstantinova and Constantin Orasan. 2013. Interactive question answering. In Emerging Applications of Natural Language Processing: Concepts and New Research. IGI Global, 149–169.

[62]

Satwik Kottur, José M. F. Moura, Devi Parikh, Dhruv Batra, and Marcus Rohrbach. 2018. Visual coreference resolution in visual dialog using neural module networks. In ECCV. 153–169.

Digital Library

[63]

Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Li Fei-Fei. 2017. Visual genome: Connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123, 1 (2017), 32–73.

Digital Library

[64]

Mayank Kulkarni and Kristy Elizabeth Boyer. 2018. Toward data-driven tutorial question answering with deep learning conversational models. In BEA@NAACL-HLT. Association for Computational Linguistics, 273–283.

[65]

Girish Kumar, Matthew Henderson, Shannon Chan, Hoang Nguyen, and Lucas Ngoo. 2018. Question-answer selection in user to user marketplace conversations. In IWSDS(Lecture Notes in Electrical Engineering, Vol. 579). Springer, 397–403.

[66]

Souvik Kundu, Qian Lin, and Hwee Tou Ng. 2020. Learning to identify follow-up questions in conversational question answering. In ACL. Association for Computational Linguistics, 959–968.

[67]

Chia-Chih Kuo, Shang-Bao Luo, and Kuan-Yu Chen. 2020. An audio-enriched bert-based framework for spoken multiple-choice question answering. In INTERSPEECH. ISCA, 4173–4177.

[68]

Veronica Latcinnik and Jonathan Berant. 2020. Explaining question answering models through text generation. CoRR abs/2004.05569 (2020).

[69]

Changyoon Lee, Donghoon Han, Hyoungwook Jin, and Alice Oh. 2019. automaTA: Human-machine interaction for answering context-specific questions. In L@S. ACM, 44:1–44:4.

Digital Library

[70]

Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L. Berg. 2018. TVQA: Localized, compositional video question answering. arXiv preprint arXiv:1809.01696 (2018).

[71]

Fei Li and H. V. Jagadish. 2014. Constructing an interactive natural language interface for relational databases. Proc. VLDB Endow. 8, 1 (2014), 73–84.

Digital Library

[72]

Huayu Li, Martin Renqiang Min, Yong Ge, and Asim Kadav. 2017. A context-aware attention network for interactive question answering. In KDD. ACM, 927–935.

Digital Library

[73]

Qing Li, Jianlong Fu, Dongfei Yu, Tao Mei, and Jiebo Luo. 2018. Tell-and-answer: Towards explainable visual question answering using attributes and captions. In EMNLP. Association for Computational Linguistics, 1338–1346.

[74]

Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, and Yifei Zhang. 2019. Answer-supervised question reformulation for enhancing conversational machine comprehension. In MRQA@EMNLP. Association for Computational Linguistics, 38–47.

[75]

Qing Li, Qingyi Tao, Shafiq R. Joty, Jianfei Cai, and Jiebo Luo. 2018. VQA-E: Explaining, elaborating, and enhancing your answers for visual questions. In ECCV(Lecture Notes in Computer Science, Vol. 11211). Springer, 570–586.

[76]

Ronghan Li, Zejun Jiang, Lifang Wang, Xinyu Lu, and Meng Zhao. 2020. Directional attention weaving for text-grounded conversational question answering. Neurocomputing 391 (2020), 13–24.

[77]

Tianle Li, Xueguang Ma, Alex Zhuang, Yu Gu, Yu Su, and Wenhu Chen. 2023. Few-shot in-context learning for knowledge base question answering. arXiv preprint arXiv:2305.01750 (2023).

[78]

Yuncheng Li, Yale Song, Liangliang Cao, Joel R. Tetreault, Larry Goldberg, Alejandro Jaimes, and Jiebo Luo. 2016. TGIF: A new dataset and benchmark on animated GIF description. In CVPR. IEEE Computer Society, 4641–4650.

[79]

Aiting Liu, Ziqi Huang, Hengtong Lu, Xiaojie Wang, and Caixia Yuan. 2019. BB-KBQA: BERT-based knowledge base question answering. In CCL(Lecture Notes in Computer Science, Vol. 11856). Springer, 81–92.

[80]

Siyuan Liu, Sourav S. Bhowmick, Wanlu Zhang, Shu Wang, and Wanyi Huang. 2018. NEURON: An interactive natural language interface for understanding query execution plans in RDBMS. CoRR abs/1805.05670 (2018).

[81]

Song Liu, Yixin Zhong, and Fuji Ren. 2013. Interactive question answering based on FAQ. In CCL(Lecture Notes in Computer Science, Vol. 8202). Springer, 73–84.

[82]

Yuhang Liu, Wei Wei, and hu. 2022. Declaration-based prompt tuning for visual question answering. In IJCAI.

[83]

James Lockett, Sanith Wijesinghe, Jasper Phillips, Ian Gross, Michael Schoenfeld, Walter T. Hiranpat, Phillip J. Marlow, Matt Coarr, and Qian Hu. 2019. Intelligent voice agent and service (iVAS) for interactive and multimodal question and answers. In FQAS(Lecture Notes in Computer Science, Vol. 11529). Springer, 396–402.

Digital Library

[84]

Vanessa Lopez, Christina Unger, Philipp Cimiano, and Enrico Motta. 2013. Evaluating question answering over linked data. J. Web Semant. 21 (2013), 3–13.

Digital Library

[85]

Anutosh Maitra, Shivam Garg, and Shubhashis Sengupta. 2020. Enabling interactive answering of procedural questions. In NLDB(Lecture Notes in Computer Science, Vol. 12089). Springer, 73–81.

[86]

Angrosh Mandya, Danushka Bollegala, and Frans Coenen. 2019. Evaluating co-reference chains based conversation history in conversational question answering. In PACLING(Communications in Computer and Information Science, Vol. 1215). Springer, 280–292.

[87]

Angrosh Mandya, James O’Neill, Danushka Bollegala, and Frans Coenen. 2020. Do not let the history haunt you: Mitigating compoundingerrors in conversational question answering. In LREC. European Language Resources Association, 2017–2025.

[88]

Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. 2019. OK-VQA: A visual question answering benchmark requiring external knowledge. In CVPR. 3195–3204.

[89]

Yosi Mass, Haggai Roitman, Shai Erera, Or Rivlin, Bar Weiner, and David Konopnicki. 2019. A study of BERT for non-factoid question-answering under passage length constraints. CoRR abs/1908.06780 (2019).

[90]

J. S. McCarley. 2019. Pruning a BERT-based question answering model. CoRR abs/1910.06360 (2019).

[91]

Pablo N. Mendes, Max Jakob, and Christian Bizer. 2012. DBpedia: A Multilingual Cross-domain Knowledge Base. European Language Resources Association (ELRA).

[92]

Tamás Mészáros and Tadeusz P. Dobrowiecki. 2017. Agent-based reconfigurable natural language interface to robotsHuman-agent interaction using task-specific controlled natural languages. In ICAART. SciTePress, 632–639.

[93]

Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018. Can a suit of armor conduct electricity? A new dataset for open book question answering. arXiv preprint arXiv:1809.02789 (2018).

[94]

Seungwhan Moon, Pararth Shah, Anuj Kumar, and Rajen Subba. 2019. Memory graph networks for explainable memory-grounded question answering. In CoNLL. Association for Computational Linguistics, 728–736.

[95]

Thomas Müller, Francesco Piccinno, Peter Shaw, Massimo Nicosia, and Yasemin Altun. 2019. Answering conversational questions on structured data without logical forms. In EMNLP/IJCNLP. Association for Computational Linguistics, 5901–5909.

[96]

M. Asif Naeem, Saif Ullah, and Imran Sarwar Bajwa. 2012. Interacting with data warehouse by using a natural language interface. In NLDB(Lecture Notes in Computer Science, Vol. 7337). Springer, 372–377.

Digital Library

[97]

Preslav Nakov, Lluís Màrquez, Walid Magdy, Alessandro Moschitti, James Glass, and Bilal Randeree. 2019. Semeval-2015 task 3: Answer selection in community question answering. arXiv preprint arXiv:1911.11403 (2019).

[98]

Allen Nie, Erin D. Bennett, and Noah D. Goodman. 2019. Learning to explain: Answering why-questions via rephrasing. CoRR abs/1906.01243 (2019).

[99]

Florian Nothdurft and Wolfgang Minker. 2016. Justification and transparency explanations in dialogue systems to maintain human-computer trust. In Situated Dialog in Speech-based Human-computer Interaction. Springer, 41–50.

[100]

Reham A. Osama, Nagwa M. El-Makky, and Marwan Torki. 2019. Question answering using hierarchical attention on top of BERT features. In MRQA@EMNLP. Association for Computational Linguistics, 191–195.

[101]

Arantxa Otegi, Aitor Gonzalez-Agirre, Jon Ander Campos, Aitor Soroa, and Eneko Agirre. 2020. Conversational question answering in low resource scenarios: A dataset and case study for basque. In LREC. European Language Resources Association, 436–442.

[102]

Jinyoung Park, Hyeong Kyu Choi, Juyeon Ko, Hyeonjin Park, Ji-Hoon Kim, Jisu Jeong, Kyungmin Kim, and Hyunwoo Kim. 2023. Relation-aware language-graph transformer for question answering. In AAAI. 13457–13464.

Digital Library

[103]

Jungin Park, Jiyoung Lee, and Kwanghoon Sohn. 2021. Bridge to answer: Structure-aware graph interaction network for video question answering. In CVPR. 15526–15535.

[104]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVE: Global vectors for word representation. In EMNLP. 1532–1543.

[105]

Rivindu Perera and Parma Nand. 2014. Interaction history based answer formulation for question answering. In KESW(Communications in Computer and Information Science, Vol. 468). Springer, 128–139.

[106]

Volha Petukhova, Desmond Darma Putra, Alexandr Chernov, and Dietrich Klakow. 2015. Understanding questions and extracting answers: Interactive quiz game application design. In LTC(Lecture Notes in Computer Science, Vol. 10930). Springer, 246–261.

[107]

Akshit Pradhan, Pragya Shukla, Pallavi Patra, Rohit Pathak, and Ajay Kumar Jena. 2018. Enhancing interaction with social networking sites for visually impaired people by using textual and visual question answering. In CICBA(Communications in Computer and Information Science, Vol. 1031). Springer, 3–14.

[108]

Zihao Qi, Dario Bertero, Ian D. Wood, and Pascale Fung. 2019. Incorporate user representation for personal question answer selection using siamese network. In ICASSP. IEEE, 7540–7544.

[109]

Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, and Mohit Iyyer. 2020. Open-retrieval conversational question answering. In SIGIR. ACM, 539–548.

Digital Library

[110]

Chen Qu, Liu Yang, Minghui Qiu, W. Bruce Croft, Yongfeng Zhang, and Mohit Iyyer. 2019. BERT with history answer embedding for conversational question answering. In SIGIR. ACM, 1133–1136.

Digital Library

[111]

Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W. Bruce Croft, and Mohit Iyyer. 2019. Attentive history selection for conversational question answering. In CIKM. ACM, 1391–1400.

Digital Library

[112]

Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don’t know: Unanswerable questions for SQuAD. In ACL. Association for Computational Linguistics, 784–789.

[113]

Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100, 000+ questions for machine comprehension of text. In EMNLP. The Association for Computational Linguistics, 2383–2392.

[114]

Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019. CoQA: A conversational question answering challenge. Trans. Assoc. Comput. Linguistics 7 (2019), 249–266.

[115]

Mengye Ren, Ryan Kiros, and Richard S. Zemel. 2015. Exploring models and data for image question answering. In NIPS. 2953–2961.

Digital Library

[116]

Heather Riley and Mohan Sridharan. 2019. Integrating non-monotonic logical reasoning and inductive learning with deep learning for explainable visual question answering. Front. Robot. AI 6 (2019), 125.

[117]

Andreas Rücklé and Iryna Gurevych. 2017. End-to-end non-factoid question answering with an interactive visualization of neural attention weights. In ACL (System Demonstrations). Association for Computational Linguistics, 19–24.

[118]

Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, and Sarath Chandar. 2018. Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph. arXiv:1801.10314 (2018).

[119]

Wataru Sakata, Tomohide Shibata, Ribeka Tanaka, and Sadao Kurohashi. 2019. FAQ retrieval using query-question similarity and BERT-based query-answer relevance. In SIGIR. ACM, 1113–1116.

Digital Library

[120]

Apoorv Saxena, Adrian Kochsiek, and Rainer Gemulla. 2022. Sequence-to-sequence knowledge graph completion and question answering. In ACL. 2814–2828.

[121]

Malte Schwarzer, Jonas Düver, Danuta Ploch, and Andreas Lommatzsch. 2016. An interactive e-government question answering system. In LWDA(CEUR Workshop Proceedings, Vol. 1670). CEUR-WS.org, 74–82.

[122]

Bhaskar Sen, Nikhil Gopal, and Xinwei Xue. 2020. Support-BERT: Predicting quality of question-answer pairs in MSDN using deep bidirectional transformer. CoRR abs/2005.08294 (2020).

[123]

Huan Shao, Yunlong Xu, Yi Ji, Jianyu Yang, and Chunping Liu. 2019. Intra-modality feature interaction using self-attention for visual question answering. In ICONIP(Communications in Computer and Information Science, Vol. 1143). Springer, 215–222.

[124]

Zhenwei Shao, Zhou Yu, Meng Wang, and Jun Yu. 2023. Prompting large language models with answer heuristics for knowledge-based visual question answering. In CVPR. 14974–14983.

[125]

Tao Shen, Xiubo Geng, Tao Qin, Daya Guo, Duyu Tang, Nan Duan, Guodong Long, and Daxin Jiang. 2019. Multi-task learning for conversational question answering over a large-scale knowledge base. In EMNLP-IJCNLP. Association for Computational Linguistics, 2442–2451.

[126]

Lei Shi, Shijie Geng, Kai Shuang, Chiori Hori, Songxiang Liu, Peng Gao, and Sen Su. 2020. Multi-layer content interaction through quaternion product for visual question answering. In ICASSP. IEEE, 4412–4416.

[127]

Andrew Shin, Yoshitaka Ushiku, and Tatsuya Harada. 2018. Customized image narrative generation via interactive visual question generation and answering. In CVPR. Computer Vision Foundation/IEEE Computer Society, 8925–8933.

[128]

Wissam Siblini, Charlotte Pasqual, Axel Lavielle, and Cyril Cauchois. 2019. Multilingual question answering from formatted text applied to conversational agents. CoRR abs/1910.04659 (2019).

[129]

Daniil Sorokin and Iryna Gurevych. 2018. Interactive instance-based evaluation of knowledge base question answering. In EMNLP (Demonstration). Association for Computational Linguistics, 114–119.

[130]

Lixin Su, Jiafeng Guo, Yixing Fan, Yanyan Lan, Ruqing Zhang, and Xueqi Cheng. 2019. An adaptive framework for conversational question answering. In AAAI. AAAI Press, 10041–10042.

Digital Library

[131]

Yu Su, Ahmed Hassan Awadallah, Miaosen Wang, and Ryen W. White. 2018. Natural language interfaces with fine-grained user interaction: A case study on web APIs. In SIGIR. ACM, 855–864.

Digital Library

[132]

Yu Su, Huan Sun, Brian Sadler, Mudhakar Srivatsa, Izzeddin Gür, Zenghui Yan, and Xifeng Yan. 2016. On generating characteristic-rich question sets for QA evaluation. In EMNLP. 562–572.

[133]

Hiroaki Sugiyama, Toyomi Meguro, and Ryuichiro Higashinaka. 2016. Evaluation of question-answering system about conversational agent’s personality. In IWSDS(Lecture Notes in Electrical Engineering, Vol. 427). Springer, 183–194.

[134]

Anirudh Sundar and Larry Heck. 2022. Multimodal conversational AI: A survey of datasets and approaches. arXiv preprint arXiv:2205.06907 (2022).

[135]

Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. 2022. Entailer: Answering questions with faithful and truthful chains of reasoning. In EMNLP. 2078–2093.

[136]

Yiming Tan, Dehai Min, Yu Li, Wenbo Li, Nan Hu, Yongrui Chen, and Guilin Qi. 2023. Can ChatGPT replace traditional KBQA models? An in-depth analysis of the question answering performance of the GPT LLM family. In International Semantic Web Conference. Springer, 348–367.

Digital Library

[137]

Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford Alpaca: An Instruction-following LLaMA model. Retrieved from https://github.com/tatsu-lab/stanford_alpaca

[138]

Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, et al. 2023. Gemini: A family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023).

[139]

Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu, and Raviteja Anantha. 2021. Question rewriting for conversational question answering. In WSDM. ACM, 355–363.

Digital Library

[140]

Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu, and Raviteja Anantha. 2021. Question rewriting for conversational question answering. In WSDM. 355–363.

Digital Library

[141]

Betty van Aken, Benjamin Winter, Alexander Löser, and Felix A. Gers. 2019. How does BERT answer questions?: A layer-wise analysis of transformer representations. In CIKM. ACM, 1823–1832.

Digital Library

[142]

Ulli Waltinger, Alexa Breuing, and Ipke Wachsmuth. 2012. Connecting question answering and conversational agents—Contextualizing German questions for interactive question answering systems. Künstliche Intell. 26, 4 (2012), 381–390.

[143]

Mengqiu Wang, Noah A. Smith, and Teruko Mitamura. 2007. What is the jeopardy model? A quasi-synchronous grammar for QA. In EMNLP-CoNLL. 22–32.

[144]

Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, and Bing Xiang. 2019. Multi-passage BERT: A globally normalized BERT model for open-domain question answering. In EMNLP/IJCNLP. Association for Computational Linguistics, 5877–5881.

[145]

Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart Van Merriënboer, Armand Joulin, and Tomas Mikolov. 2015. Towards AI-complete question answering: A set of prerequisite toy tasks. arXiv preprint arXiv:1502.05698 (2015).

[146]

Wilson Wong, Lawrence Cavedon, John Thangarajah, and Lin Padgham. 2012. Mixed-initiative conversational system using question-answer pairs mined from the web. In CIKM. ACM, 2707–2709.

Digital Library

[147]

Wilson Wong, Lawrence Cavedon, John Thangarajah, and Lin Padgham. 2012. Strategies for mixed-initiative conversation management using question-answer pairs. In COLING. Indian Institute of Technology Bombay, 2821–2834.

[148]

Wilson Wong, John Thangarajah, and Lin Padgham. 2011. Health conversational system based on contextual matching of community-driven question-answer pairs. In CIKM. ACM, 2577–2580.

Digital Library

[149]

Fei Wu, Xinyu Duan, Jun Xiao, Zhou Zhao, Siliang Tang, Yin Zhang, and Yueting Zhuang. 2017. Temporal interaction and causal influence in community-based question answering. IEEE Trans. Knowl. Data Eng. 29, 10 (2017), 2304–2317.

Digital Library

[150]

Jinmeng Wu, Tingting Mu, Jeyarajan Thiyagalingam, and John Yannis Goulermas. 2020. Building interactive sentence-aware representation based on generative language model for community question answering. Neurocomputing 389 (2020), 93–107.

[151]

Zhiyong Wu, Ben Kao, Tien-Hsuan Wu, Pengcheng Yin, and Qun Liu. 2020. PERQ: Predicting, explaining, and rectifying failed questions in KB-QA systems. In WSDM. ACM, 663–671.

Digital Library

[152]

Zhipeng Xie. 2017. Enhancing document-based question answering via interaction between question words and POS tags. In NLPCC(Lecture Notes in Computer Science, Vol. 10619). Springer, 136–147.

[153]

Kun Xiong, Anqi Cui, Zefeng Zhang, and Ming Li. 2016. Neural contextual conversation learning with labeled question-answering pairs. CoRR abs/1607.05809 (2016).

[154]

Jun Xu, Tao Mei, Ting Yao, and Yong Rui. 2016. MSR-VTT: A large video description dataset for bridging video and language. In CVPR. IEEE Computer Society, 5288–5296.

[155]

Jingjing Xu, Yuechen Wang, Duyu Tang, Nan Duan, Pengcheng Yang, Qi Zeng, Ming Zhou, and Xu Sun. 2019. Asking clarification questions in knowledge-based question answering. In EMNLP-IJCNLP. Association for Computational Linguistics, 1618–1629.

[156]

Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019. End-to-end open-domain question answering with BERTserini. In NAACL-HLT (Demonstrations). Association for Computational Linguistics, 72–77.

[157]

Wei Yang, Yuqing Xie, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019. Data augmentation for BERT fine-tuning in open-domain question answering. CoRR abs/1904.06652 (2019).

[158]

Yi Yang, Wen-tau Yih, and Christopher Meek. 2015. WikiQA: A challenge dataset for open-domain question answering. In EMNLP. 2013–2018.

[159]

Zekun Yang, Noa Garcia, Chenhui Chu, Mayu Otani, Yuta Nakashima, and Haruo Takemura. 2020. BERT representations for video question answering. In WACV. IEEE, 1545–1554.

[160]

Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600 (2018).

[161]

Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh. 2016. The value of semantic parse labeling for knowledge base question answering. In ACL. 201–206.

[162]

Xingdi Yuan, Marc-Alexandre Côté, Jie Fu, Zhouhan Lin, Chris Pal, Yoshua Bengio, and Adam Trischler. 2019. Interactive language learning by question answering. In EMNLP/IJCNLP. Association for Computational Linguistics, 2796–2813.

[163]

Oksana Zdrok. 2023. The Rise of Generative Question Answering with LLMs. Retrieved from https://shelf.io/blog/the-rise-of-generative-question-answering-with-llms/

[164]

Hongzhi Zhang, Guandong Xu, Xiao Liang, Tinglei Huang, and Kun Fu. 2018. An attention-based word-level interaction model: Relation detection for knowledge base question answering. CoRR abs/1801.09893 (2018).

[165]

Sheng Zhang, Xin Zhang, Hui Wang, Jiajun Cheng, Pei Li, and Zhaoyun Ding. 2017. Chinese medical question answer matching using end-to-end character-level multi-Scale CNNs. Appl. Sci. 7, 8 (2017), 767.

[166]

Sheng Zhang, Xin Zhang, Hui Wang, Lixiang Guo, and Shanshan Liu. 2018. Multi-scale attentive interaction networks for chinese medical question answer selection. IEEE Access 6 (2018), 74061–74071.

[167]

Xinbo Zhang, Lei Zou, and Sen Hu. 2019. An interactive mechanism to improve question answering systems via feedback. In CIKM. ACM, 1381–1390.

Digital Library

[168]

Yingying Zhang, Shengsheng Qian, Quan Fang, and Changsheng Xu. 2019. Multi-modal knowledge-aware hierarchical attention network for explainable medical question answering. In ACM Multimedia. ACM, 1089–1097.

Digital Library

[169]

Weiguo Zheng, Hong Cheng, Jeffrey Xu Yu, Lei Zou, and Kangfei Zhao. 2019. Interactive natural language question answering over knowledge graphs. Inf. Sci. 481 (2019), 141–159.

Digital Library

[170]

Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, and Nan Duan. 2022. ProQA: Structural prompt-based pre-training for unified question answering. In NAACL-HLT. 4230–4243.

[171]

Guangyou Zhou, Yin Zhou, Tingting He, and Wensheng Wu. 2016. Learning semantic representation with neural networks for community question answering retrieval. Knowl.-Based Syst. 93 (2016), 75–83.

Digital Library

[172]

Zhiheng Zhou, Man Lan, Yuanbin Wu, and Jun Lang. 2017. Single turn Chinese emotional conversation generation based on information retrieval and question answering. In IALP. IEEE, 103–106.

[173]

Chenguang Zhu, Michael Zeng, and Xuedong Huang. 2018. SDNet: Contextualized attention-based deep network for conversational question answering. CoRR abs/1812.03593 (2018).

[174]

Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. 2016. Visual7W: Grounded question answering in images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4995–5004.

Cited By

Kostopoulos GDavrazos GKotsiantis S(2024)Explainable Artificial Intelligence-Based Decision Support Systems: A Recent ReviewElectronics10.3390/electronics1314284213:14(2842)Online publication date: 19-Jul-2024
https://doi.org/10.3390/electronics13142842
Trčková KMaršálek RŽáček MTeplá M(2024)Why Do Fishes Die? Lab Assignment Created Using ChatGPTJournal of Chemical Education10.1021/acs.jchemed.4c00130101:8(3171-3178)Online publication date: 15-Jul-2024
https://doi.org/10.1021/acs.jchemed.4c00130

Index Terms

Interactive Question Answering Systems: Literature Review

Recommendations

Quality-aware collaborative question answering: methods and evaluation
WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining

Community Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this ...
A Systematic Literature Review of Question Answering: Research Trends, Datasets, Methods
Computational Science and Its Applications – ICCSA 2022 Workshops
Abstract
Answering questions, finding the most appropriate answer to the question given by the user as input are among the important tasks of natural language processing. Many studies have been done on question answering and datasets, methods have been ...
An Interactive Mechanism to Improve Question Answering Systems via Feedback
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Semantic parsing-based RDF question answering (QA) systems are to interpret users' natural language questions as query graphs and return answers over RDF repository. However, due to the complexity of linking natural phrases with specific RDF items (e.g.,...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 9

September 2024

980 pages

EISSN:1557-7341

DOI:10.1145/3613649

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2024

Online AM: 11 April 2024

Accepted: 02 April 2024

Revised: 08 March 2024

Received: 17 July 2022

Published in CSUR Volume 56, Issue 9

Check for updates

Author Tags

Qualifiers

Survey

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
387
Total Downloads

Downloads (Last 12 months)387
Downloads (Last 6 weeks)83

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kostopoulos GDavrazos GKotsiantis S(2024)Explainable Artificial Intelligence-Based Decision Support Systems: A Recent ReviewElectronics10.3390/electronics1314284213:14(2842)Online publication date: 19-Jul-2024
https://doi.org/10.3390/electronics13142842
Trčková KMaršálek RŽáček MTeplá M(2024)Why Do Fishes Die? Lab Assignment Created Using ChatGPTJournal of Chemical Education10.1021/acs.jchemed.4c00130101:8(3171-3178)Online publication date: 15-Jul-2024
https://doi.org/10.1021/acs.jchemed.4c00130

View Options

Get Access

Login options

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

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents