Automatic Generation Of Natural Language Explanations

Felipe Costa, Sixun Ouyang, Peter Dolog, Aonghus Lawlor . Companion Proceedings of the 23rd International Conference on Intelligent User Interfaces 2018 – 87 citations

[Paper]    
Compositional Generalization Content Enrichment Datasets Evaluation Image Text Integration Interactive Environments Interdisciplinary Approaches Model Architecture Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering

An important task for recommender system is to generate explanations according to a user’s preferences. Most of the current methods for explainable recommendations use structured sentences to provide descriptions along with the recommendations they produce. However, those methods have neglected the review-oriented way of writing a text, even though it is known that these reviews have a strong influence over user’s decision. In this paper, we propose a method for the automatic generation of natural language explanations, for predicting how a user would write about an item, based on user ratings from different items’ features. We design a character-level recurrent neural network (RNN) model, which generates an item’s review explanations using long-short term memories (LSTM). The model generates text reviews given a combination of the review and ratings score that express opinions about different factors or aspects of an item. Our network is trained on a sub-sample from the large real-world dataset BeerAdvocate. Our empirical evaluation using natural language processing metrics shows the generated text’s quality is close to a real user written review, identifying negation, misspellings, and domain specific vocabulary.

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