Abstract
As machine learning models that underlie machine reading systems are becoming more complex, latent, and end-to-end, they are also becoming less interpretable and controllable. In times of rule-based systems users could interact with a system in a two-way fashion: injecting their own background knowledge into the system through explanations in the form of rules, and extracting explanations from the system in the form of derivations. It is not clear how this type of communication can be realized within more modern architectures. In this position paper we propose a research agenda that will (re-)enable this two-way communication with machine readers while maintaining the benefits of the models of today. In fact, we argue for a paradigm in which the machine reading system is an agent that communicates with us, learning from our examples and explanations, and providing us with explanations for its decisions we can use to debug and improve the agent further.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: Association for Computational Linguistics (ACL) (2014)
Chiticariu, L., Li, Y., Reiss, F.R.: Rule-based information extraction is dead! long live rule-based information extraction systems! In: EMNLP, pp. 827–832, ACL (2013). http://dblp.uni-trier.de/db/conf/emnlp/emnlp2013.html#ChiticariuLR13
Craven, M.W., Shavlik, J.W.: Extracting tree-structured representations of trained networks. In: Advances in Neural Information Processing Systems (NIPS-8), pp. 24–30 (1996)
Culotta, A., Sorensen, J.: Dependency tree kernels for relation extraction. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), Barcelona, Spain (2004). http://www.cs.umass.edu/culotta/pubs/tkernel.pdf
Ganchev, K., Graca, J., Gillenwater, J., Taskar, B.: Posterior regularization for structured latent variable models. Technical report MS-CIS-09-16, University of Pennsylvania Department of Computer and Information Science (2009)
Gardner, M., Talukdar, P., Krishnamurthy, J., Mitchell, T.: Incorporating vector space similarity in random walk inference over knowledge bases. In: Proceedings of EMNLP (2014)
Mann, G.S., McCallum, A.: Generalized expectation criteria for semi-supervised learning of conditional random fields. In: Annual Meeting of the Association for Computational Linguistics (ACL), pp. 870–878 (2008)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (ACL 2009), pp. 1003–1011. Association for Computational Linguistics (2009)
Neelakantan, A., Roth, B., McCallum, A.: Compositional vector space models for knowledge base completion. In: Association for Computational Linguistics (ACL) (2015)
Reddy, S., Lapata, M., Steedman, M.: Large-scale semantic parsing without question-answer pairs. Trans. Assoc. Comput. Linguist. (TACL) 2, 377–392 (2014)
Riedel, S., Yao, L., Marlin, B.M., McCallum, A.: Relation extraction with matrix factorization and universal schemas. In: Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2013), June 2013
Rocktäschel, T., Singh, S., Riedel, S.: Injecting logical background knowledge into embeddings for relation extraction. In: Proceedings of the 2015 Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (2015)
Sanchez, I., Rocktaschel, T., Riedel, S., Singh, S.: Towards extracting faithful and descriptive representations of latent variable models. In: AAAI Spring Symposium on Knowledge Representation and Reasoning, March 2015
Singh, S., Hillard, D., Leggetter, C.: Minimally-supervised extraction of entities from text advertisements. In: North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT) (2010)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)
Suchanek, F., Fan, J., Hoffmann, R., Riedel, S., Talukdar, P.P.: Advances in automated knowledge base construction. In: SIGMOD Records Journal, March 2013. http://suchanek.name/work/publications/sigmodrec2013akbc
Thrun, S.: Extracting rules from artificial neural networks with distributed representations. In: Advances in Neural Information Processing Systems, pp. 505–512 (1995)
Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing text for joint embedding of text and knowledge bases. In: ACL Association for Computational Linguistics, September 2015. http://research.microsoft.com/apps/pubs/default.aspx?id=254916
Weston, J., Chopra, S., Bordes, A.: Memory networks. CoRR abs/1410.3916 (2014). http://arxiv.org/abs/1410.3916
Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In: Uncertainty in Artifical Intelligence (UAI) (2005)
Acknowledgments
This work was supported in part by Microsoft Research through its PhD Scholarship Programme, in part by CONAYCT, in part by the TerraSwarm Research Center, one of six centers supported by the STARnet phase of the Focus Center Research Program (FCRP) a Semiconductor Research Corporation program sponsored by MARCO and DARPA, in part by an ARO grant number W911NF-13-1-0246, and in part by the Paul Allen Foundation through an Allen Distinguished Investigator grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Riedel, S., Singh, S., Bouchard, G., Rocktäschel, T., Sanchez, I. (2015). Towards Two-Way Interaction with Reading Machines. In: Dediu, AH., Martín-Vide, C., Vicsi, K. (eds) Statistical Language and Speech Processing. SLSP 2015. Lecture Notes in Computer Science(), vol 9449. Springer, Cham. https://doi.org/10.1007/978-3-319-25789-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-25789-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25788-4
Online ISBN: 978-3-319-25789-1
eBook Packages: Computer ScienceComputer Science (R0)