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Register a new userI Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Review
أتمنى لو كنت أحببت هذا واحد، لكنني لم أفعل - مجموعة بيانات متعددة اللغات للكشف عن الوسائل في مراجعة المنتج
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تصفيات مضادة تصف الأحداث التي لم تتم أو لا يمكنها إجراءها. نحن نعتبر مشكلة الكشف المتعرضين (CFD) في مراجعات المنتج. لهذا الغرض، فإننا نحيطر على مجموعة بيانات متعددة اللغات CFD من مراجعات منتجات الأمازون التي تغطي البيانات الإضافية المكتوب باللغات الإنجليزية والألمانية واليابانية. DataSet فريدة من نوعها لأنها تحتوي على مضادة بلغات متعددة، ويغطي مساحة تطبيق جديدة من مراجعات التجارة الإلكترونية، وتوفر شروح محترفة عالية الجودة. نقوم بتدريب نماذج CFD باستخدام طرق وأساليب تمثيل نصية مختلفة. نجد أن هذه النماذج قوية ضد التحيزات الاجتماعية التي تم تقديمها بسبب اختيار الجملة التي تعتمد على العبارات. علاوة على ذلك، فإن مجموعة بيانات CFD الخاصة بنا متوافقة مع مجموعات البيانات السابقة ويمكن دمجها لتعلم نماذج CFD دقيقة. تطبيق الترجمة الآلية على الأمثلة الإنجليزية المضادة لإنشاء بيانات متعددة اللغات يؤدي بشكل سيء، مما يدل على خصوصية لغة هذه المشكلة، والتي تم تجاهلها حتى الآن.
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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