Automatic Generation of Natural Language Explanations

Felipe Soares Da Costa, Sixun Ouyang, Peter Dolog, Aonghus Lawlor

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

7 Citations (Scopus)

Abstract

An interesting challenge for explainable recommender systems is to provide successful interpretation of recommendations using structured sentences. It is well known that user-generated reviews, have strong influence on the users' decision. Recent techniques exploit user reviews to generate natural language explanations. In this paper, we propose a character-level attention-enhanced long short-term memory model to generate natural language explanations. We empirically evaluated this network using two real-world review datasets. The generated text present readable and similar to a real user's writing, due to the ability of reproducing negation, misspellings, and domain-specific vocabulary.
Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Intelligent User Interfaces
PublisherAssociation for Computing Machinery
Publication date8 Mar 2018
Article number57
ISBN (Electronic)978-1-4503-5571-1
DOIs
Publication statusPublished - 8 Mar 2018
EventInternational Conference on Intelligent User Interfaces - Hitotsubashi Hall (National Center of Sciences Building), Tokyo, Japan
Duration: 7 Mar 201811 Mar 2018
Conference number: 23
https://iui.acm.org/2018/

Conference

ConferenceInternational Conference on Intelligent User Interfaces
Number23
LocationHitotsubashi Hall (National Center of Sciences Building)
CountryJapan
CityTokyo
Period07/03/201811/03/2018
Internet address

Fingerprint

Recommender systems
Long short-term memory

Keywords

  • Explainability
  • Explanations
  • Natural language generation
  • Neural network
  • Recommender systems

Cite this

Da Costa, F. S., Ouyang, S., Dolog, P., & Lawlor, A. (2018). Automatic Generation of Natural Language Explanations. In Proceedings of the 23rd International Conference on Intelligent User Interfaces [57] Association for Computing Machinery. https://doi.org/10.1145/3180308.3180366
Da Costa, Felipe Soares ; Ouyang, Sixun ; Dolog, Peter ; Lawlor, Aonghus. / Automatic Generation of Natural Language Explanations. Proceedings of the 23rd International Conference on Intelligent User Interfaces. Association for Computing Machinery, 2018.
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Da Costa, FS, Ouyang, S, Dolog, P & Lawlor, A 2018, Automatic Generation of Natural Language Explanations. in Proceedings of the 23rd International Conference on Intelligent User Interfaces., 57, Association for Computing Machinery, International Conference on Intelligent User Interfaces, Tokyo, Japan, 07/03/2018. https://doi.org/10.1145/3180308.3180366

Automatic Generation of Natural Language Explanations. / Da Costa, Felipe Soares; Ouyang, Sixun; Dolog, Peter; Lawlor, Aonghus.

Proceedings of the 23rd International Conference on Intelligent User Interfaces. Association for Computing Machinery, 2018. 57.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AU - Da Costa, Felipe Soares

AU - Ouyang, Sixun

AU - Dolog, Peter

AU - Lawlor, Aonghus

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N2 - An interesting challenge for explainable recommender systems is to provide successful interpretation of recommendations using structured sentences. It is well known that user-generated reviews, have strong influence on the users' decision. Recent techniques exploit user reviews to generate natural language explanations. In this paper, we propose a character-level attention-enhanced long short-term memory model to generate natural language explanations. We empirically evaluated this network using two real-world review datasets. The generated text present readable and similar to a real user's writing, due to the ability of reproducing negation, misspellings, and domain-specific vocabulary.

AB - An interesting challenge for explainable recommender systems is to provide successful interpretation of recommendations using structured sentences. It is well known that user-generated reviews, have strong influence on the users' decision. Recent techniques exploit user reviews to generate natural language explanations. In this paper, we propose a character-level attention-enhanced long short-term memory model to generate natural language explanations. We empirically evaluated this network using two real-world review datasets. The generated text present readable and similar to a real user's writing, due to the ability of reproducing negation, misspellings, and domain-specific vocabulary.

KW - Explainability

KW - Explanations

KW - Natural language generation

KW - Neural network

KW - Recommender systems

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Da Costa FS, Ouyang S, Dolog P, Lawlor A. Automatic Generation of Natural Language Explanations. In Proceedings of the 23rd International Conference on Intelligent User Interfaces. Association for Computing Machinery. 2018. 57 https://doi.org/10.1145/3180308.3180366