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      Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Neural Domain Adaptation

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          Abstract

          Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our neural domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-review samples, would perform better than our proposed baselines, at answering specific, subjective product-related queries using reviews. We validate this hypothesis using a small gold standard dataset of question-review pairs evaluated by human experts, significantly surpassing our chosen baselines. Moreover, our approach, using no labeled question-review sentence pair data for training, gives performance at par with another method utilizing labeled question-review samples for the same task.

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          A theory of learning from different domains

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            Personalized recommendation on dynamic content using predictive bilinear models

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              Addressing Complex and Subjective Product-Related Queries with Customer Reviews

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                Author and article information

                Journal
                18 October 2019
                Article
                1910.08270
                c603bdd0-eaaa-41c2-8341-7d363e08370f

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                8 pages, 4 figures, 6 tables
                cs.CL cs.IR cs.LG

                Theoretical computer science,Information & Library science,Artificial intelligence

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