Fetching the paper…
Reading the bibliography…
A question answering (QA) system is a type of conversational AI that generates natural language answers to questions posed by human users.
A. M. Logar, E. M. Corwin, and W. J. Oldham, “A comparison of recurrent neural network learning algorithms,” in IEEE International Conference on Neural Networks . IEEE, 1993, pp. 1129–1134
1993
Earlier work this paper cites.
M. A. Walker, D. J. Litman, C. A. Kamm, and A. Abella, “Evaluating interactive dialogue systems: Extending component evaluation to integrated system evaluation,” in Interactive Spoken Dialog Systems: Bringing Speech and NLP Together in Real Applications , 1997
1997
Earlier work this paper cites.
S. Burger, K. Weilhammer, F. Schiel, and H. G. Tillmann, “Verbmobil data collection and annotation,” in Verbmobil: Foundations of speech-to-speech translation . Springer, 2000, pp. 537–549
2000
Earlier work this paper cites.
E. Reiter and R. Dale, Building natural language generation systems . New York, NY, USA: Cambridge University Press, 2000
2000
Earlier work this paper cites.
N. Beringer, U. Kartal, K. Louka, F. Schiel, U. Türk et al. , “Promise: A procedure for multimodal interactive system evaluation,” in Proceedings of the Workshop’Multimodal Resources and Multimodal Systems Evaluation . Citeseer, 2002, pp. 90–95
2002
Earlier work this paper cites.
K. Doya, “Recurrent networks: learning algorithms,” The Handbook of Brain Theory and Neural Networks, , pp. 955–960, 2003
2003
Earlier work this paper cites.
A. J. Ko and B. A. Myers, “Designing the whyline: a debugging interface for asking questions about program behavior,” in Proceedings of the SIGCHI conference on Human factors in computing systems . ACM, 2004, pp. 151–158
2004
Earlier work this paper cites.
R. Craggs and M. M. Wood, “Evaluating discourse and dialogue coding schemes,” Computational Linguistics , vol. 31, no. 3, pp. 289–296, 2005
2005
Earlier work this paper cites.
E. Linstead, S. Bajracharya, T. Ngo, P. Rigor, C. Lopes, and P. Baldi, “Sourcerer: mining and searching internet-scale software repositories,” Data Mining and Knowledge Discovery , vol. 18, pp. 300–336, 2009
2009
Earlier work this paper cites.
G. Sridhara, E. Hill, D. Muppaneni, L. Pollock, and K. Vijay-Shanker, “Towards automatically generating summary comments for java methods,” in Proceedings of the IEEE/ACM international conference on Automated software engineering . ACM, 2010, pp. 43–52
2010
Earlier work this paper cites.
V. Rieser and O. Lemon, Reinforcement learning for adaptive dialogue systems: a data-driven methodology for dialogue management and natural language generation . Springer Science & Business Media, 2011
2011
Earlier work this paper cites.
O. Lemon, “Learning what to say and how to say it: Joint optimisation of spoken dialogue management and natural language generation,” Computer Speech & Language , vol. 25, no. 2, pp. 210–221, 2011
2011
Earlier work this paper cites.
M. P. Robillard and R. Deline, “A field study of api learning obstacles,” Empirical Software Engineering , vol. 16, no. 6, pp. 703–732, 2011
2011
Earlier work this paper cites.
G. Sridhara, L. Pollock, and K. Vijay-Shanker, “Automatically detecting and describing high level actions within methods,” in Proceedings of the 33rd International Conference on Software Engineering . ACM, 2011, pp. 101–110
2011
Earlier work this paper cites.
M. Monperrus, M. Eichberg, E. Tekes, and M. Mezini, “What should developers be aware of? an empirical study on the directives of api documentation,” Empirical Software Engineering , vol. 17, no. 6, pp. 703–737, 2012
2012
Earlier work this paper cites.
P. Blunsom, N. Kalchbrenner, and N. Kalchbrenner, “Recurrent convolutional neural networks for discourse compositionality,” in Proceedings of the 2013 Workshop on Continuous Vector Space Models and their Compositionality . Proceedings of the 2013 Workshop on Continuous Vector Space Models and their Compositionality, 2013
2013
Earlier work this paper cites.
W. Maalej and M. P. Robillard, “Patterns of knowledge in api reference documentation,” IEEE Transactions on Software Engineering , vol. 39, no. 9, pp. 1264–1282, 2013
2013
Earlier work this paper cites.
W. Maalej, R. Tiarks, T. Roehm, and R. Koschke, “On the comprehension of program comprehension,” ACM Transactions on Software Engineering and Methodology (TOSEM) , vol. 23, no. 4, p. 31, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Malinowski, M. Rohrbach, and M. Fritz, “Ask your neurons: A neural-based approach to answering questions about images,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1–9
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
V. Arnaoudova, S. Haiduc, A. Marcus, and G. Antoniol, “The use of text retrieval and natural language processing in software engineering,” in Proceedings of the 37th International Conference on Software Engineering-Volume 2 . IEEE Press, 2015, pp. 949–950
2015
Earlier work this paper cites.
P. Pruski, S. Lohar, W. Goss, A. Rasin, and J. Cleland-Huang, “Tiqi: answering unstructured natural language trace queries,” Requirements Engineering , vol. 20, no. 3, pp. 215–232, 2015
2015
Cited alongside, same era.
J. Yin, X. Jiang, Z. Lu, L. Shang, H. Li, and X. Li, “Neural generative question answering,” in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence . AAAI Press, 2016, pp. 2972–2978
2016
Cited alongside, same era.
N. G. Ward and D. DeVault, “Challenges in building highly-interactive dialog systems,” AI Magazine , vol. 37, no. 4, pp. 7–18, 2016
2016
Cited alongside, same era.
A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwińska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou et al. , “Hybrid computing using a neural network with dynamic external memory,” Nature , vol. 538, no. 7626, p. 471, 2016
2016
Cited alongside, same era.
N. C. Bradley, T. Fritz, and R. Holmes, “Context-aware conversational developer assistants,” in Proceedings of the 40th International Conference on Software Engineering . ACM, 2018, pp. 993–1003
2018
Later among the works it cites.
T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” ieee Computational intelligenCe magazine , vol. 13, no. 3, pp. 55–75, 2018
2018
Later among the works it cites.
S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M. P. Reyes, M.-L. Shyu, S.-C. Chen, and S. Iyengar, “A survey on deep learning: Algorithms, techniques, and applications,” ACM Computing Surveys (CSUR) , vol. 51, no. 5, p. 92, 2018
2018
Later among the works it cites.
S. A. Hayati, R. Olivier, P. Avvaru, P. Yin, A. Tomasic, and G. Neubig, “Retrieval-based neural code generation,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , 2018, pp. 925–930
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Xiong, S. Merity, and R. Socher, “Dynamic memory networks for visual and textual question answering,” in International conference on machine learning , 2016, pp. 2397–2406
2016
Cited alongside, same era.
A. M. Lamb, A. G. A. P. Goyal, Y. Zhang, S. Zhang, A. C. Courville, and Y. Bengio, “Professor forcing: A new algorithm for training recurrent networks,” in Advances In Neural Information Processing Systems , 2016, pp. 4601–4609
2016
Cited alongside, same era.
P. W. McBurney and C. McMillan, “Automatic source code summarization of context for java methods,” IEEE Transactions on Software Engineering , vol. 42, no. 2, pp. 103–119, 2016
2016
Cited alongside, same era.
W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing , vol. 234, pp. 11–26, 2017
2017
Cited alongside, same era.
H. Chen, X. Liu, D. Yin, and J. Tang, “A survey on dialogue systems: Recent advances and new frontiers,” Acm Sigkdd Explorations Newsletter , vol. 19, no. 2, pp. 25–35, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. P. Robillard, A. Marcus, C. Treude, G. Bavota, O. Chaparro, N. Ernst, M. A. Gerosa, M. Godfrey, M. Lanza, M. Linares-Vásquez et al. , “On-demand developer documentation,” in 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2017, pp. 479–483
2017
Cited alongside, same era.
Y. Tian, F. Thung, A. Sharma, and D. Lo, “Apibot: Question answering bot for api documentation,” in 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2017, pp. 153–158
2017
Cited alongside, same era.
H. J. Zhao and J. Liu, “Finding answers from the word of god: Domain adaptation for neural networks in biblical question answering,” in 2018 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2018, pp. 1–8
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Gao, M. Galley, L. Li et al. , “Neural approaches to conversational ai,” Foundations and Trends® in Information Retrieval , vol. 13, no. 2-3, pp. 127–298, 2019
2019
Later among the works it cites.
M. Johnson and A. Vera, “No ai is an island: The case for teaming intelligence,” AI Magazine , vol. 40, no. 1, pp. 16–28, 2019
2019
Later among the works it cites.
Y. Chen, L. Wu, and M. J. Zaki, “Bidirectional attentive memory networks for question answering over knowledge bases,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers , 2019, pp. 2913–2923
2019
Later among the works it cites.
2019
Later among the works it cites.
Z. Lin, X. Huang, F. Ji, H. Chen, and Y. Zhang, “Task-oriented conversation generation using heterogeneous memory networks,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing , 2019
2019
Later among the works it cites.
A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,” IEEE Access , vol. 7, pp. 53 040–53 065, 2019
2019
Later among the works it cites.
A. LeClair, S. Jiang, and C. McMillan, “A neural model for generating natural language summaries of program subroutines,” in Proceedings of the 41st International Conference on Software Engineering . IEEE Press, 2019, pp. 795–806
2019
Later among the works it cites.
Z. Chen, S. J. Kommrusch, M. Tufano, L.-N. Pouchet, D. Poshyvanyk, and M. Monperrus, “Sequencer: Sequence-to-sequence learning for end-to-end program repair,” IEEE Transactions on Software Engineering , 2019
2019
Later among the works it cites.
A. LeClair and C. McMillan, “Recommendations for datasets for source code summarization,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 3931–3937
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Wan, J. Shu, Y. Sui, G. Xu, Z. Zhao, J. Wu, and P. S. Yu, “Multi-modal attention network learning for semantic source code retrieval,” in Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering , ser. ASE ’19. IEEE Press, 2019, p. 13–25. [Online]. Available: https://doi.org/10.1109/ASE.2019.00012
2019
Later among the works it cites.
Z. Eberhart, A. Bansal, and C. McMillan, “The apiza corpus: Api usage dialogues with a simulated virtual assistant,” 2020
2020
Later among the works it cites.
H. Ed-Douibi, G. Daniel, and J. Cabot, “Openapi bot: A chatbot to help you understand rest apis,” in Web Engineering , M. Bielikova, T. Mikkonen, and C. Pautasso, Eds. Cham: Springer International Publishing, 2020, pp. 538–542
2020
Later among the works it cites.
W. Yu, L. Wu, Q. Zeng, Y. Deng, S. Tao, and M. Jiang, “Crossing variational autoencoders for answer retrieval,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , 2020, pp. 5635–5641
2020
Later among the works it cites.