poster

Automatic Generation of Natural Language Explanations

Authors:

Felipe Costa,

Sixun Ouyang,

Peter Dolog,

Aonghus LawlorAuthors Info & Claims

IUI '18 Companion: Companion Proceedings of the 23rd International Conference on Intelligent User Interfaces

Article No.: 57, Pages 1 - 2

https://doi.org/10.1145/3180308.3180366

Published: 05 March 2018 Publication History

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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.

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Cited By

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Zhang ZDastani MSichman JAlechina NDignum V(2024)Advancing Sample Efficiency and Explainability in Multi-Agent Reinforcement LearningProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663288(2791-2793)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663288
Breve BCimino GDeufemia V(2024)Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation RulesACM Transactions on Intelligent Systems and Technology10.1145/3675401Online publication date: 29-Jun-2024
https://doi.org/10.1145/3675401
Tian YKummerfeld JLi TZhang T(2024)SQLucid: Grounding Natural Language Database Queries with Interactive ExplanationsProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676368(1-20)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676368
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Index Terms

Automatic Generation of Natural Language Explanations
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches

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Published In

IUI '18 Companion: Companion Proceedings of the 23rd International Conference on Intelligent User Interfaces

March 2018

141 pages

ISBN:9781450355711

DOI:10.1145/3180308

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 March 2018

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Poster
Research
Refereed limited

Funding Sources

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Science Foundation Ireland (SFI)

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IUI'18

Sponsor:

IUI'18: 23rd International Conference on Intelligent User Interfaces

March 7 - 11, 2018

Tokyo, Japan

Acceptance Rates

IUI '18 Companion Paper Acceptance Rate 63 of 127 submissions, 50%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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IUI '25

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30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

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Zhang ZDastani MSichman JAlechina NDignum V(2024)Advancing Sample Efficiency and Explainability in Multi-Agent Reinforcement LearningProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663288(2791-2793)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663288
Breve BCimino GDeufemia V(2024)Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation RulesACM Transactions on Intelligent Systems and Technology10.1145/3675401Online publication date: 29-Jun-2024
https://doi.org/10.1145/3675401
Tian YKummerfeld JLi TZhang T(2024)SQLucid: Grounding Natural Language Database Queries with Interactive ExplanationsProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676368(1-20)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676368
Zhuang HZhang WChen WYang JSheng Q(2024)Improving Faithfulness and Factuality with Contrastive Learning in Explainable RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365398416:1(1-23)Online publication date: 26-Dec-2024
https://dl.acm.org/doi/10.1145/3653984
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Ning ZTian YZhang ZZhang TLi T(2024)Insights into Natural Language Database Query Errors: from Attention Misalignment to User Handling StrategiesACM Transactions on Interactive Intelligent Systems10.1145/365011414:4(1-32)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3650114
Zhang JTang JChen XYu WHu LJiang PLi HBaeza-Yates RBonchi F(2024)Natural Language Explainable Recommendation with Robustness EnhancementProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671781(4203-4212)Online publication date: 25-Aug-2024
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Łajewska WSpina DTrippas JBalog KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Explainability for Transparent Conversational Information-SeekingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657768(1040-1050)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657768
Liu XYu TXie KWu JLi SAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Interact with the Explanations: Causal Debiased Explainable Recommendation SystemProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635855(472-481)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635855
Damo GOcampo NCabrio EVillata S(2024)Unveiling the Hate: Generating Faithful and Plausible Explanations for Implicit and Subtle Hate Speech DetectionNatural Language Processing and Information Systems10.1007/978-3-031-70239-6_15(211-225)Online publication date: 20-Sep-2024
https://doi.org/10.1007/978-3-031-70239-6_15
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References

Cited By

Index Terms

Recommendations

Generate Neural Template Explanations for Recommendation

LLM-generated Explanations for Recommender Systems

UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation

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