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I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews

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arxiv 2104.06893 v2 pith:PJR3B4CZ submitted 2021-04-14 cs.CL cs.AIcs.LG

James O'Neill , Polina Rozenshtein , Ryuichi Kiryo , Motoko Kubota , Danushka Bollegala This is my paper
classification cs.CLcs.AIcs.LG
keywords counterfactualdatasetreviewsmodelsmultilingualproductdetectionenglish
verification ladder T0 review T1 audit T2 compute T3 formal
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Counterfactual statements describe events that did not or cannot take place. We consider the problem of counterfactual detection (CFD) in product reviews. For this purpose, we annotate a multilingual CFD dataset from Amazon product reviews covering counterfactual statements written in English, German, and Japanese languages. The dataset is unique as it contains counterfactuals in multiple languages, covers a new application area of e-commerce reviews, and provides high quality professional annotations. We train CFD models using different text representation methods and classifiers. We find that these models are robust against the selectional biases introduced due to cue phrase-based sentence selection. Moreover, our CFD dataset is compatible with prior datasets and can be merged to learn accurate CFD models. Applying machine translation on English counterfactual examples to create multilingual data performs poorly, demonstrating the language-specificity of this problem, which has been ignored so far.

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Cited by 3 Pith papers

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