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Register a new userInvestigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI
التحقيق في تأثير تفسيرات اللغة الطبيعية على التعميم الخارجي في NLI
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على الرغم من أن النماذج العصبية قد أظهرت أداء قويا في مجموعات البيانات مثل SNLI، إلا أنها تفتقر إلى القدرة على التعميم خارج التوزيع (OOD).في هذا العمل، نقوم بصياغة عدد قليل من إعدادات التعلم ودراسة آثار تفسيرات اللغة الطبيعية على تعميم OOD.نحن نستفيد من القوالب في DataSet Hans وبناء تفسيرات لغة طبيعية TEMPLated لكل قالب.على الرغم من أن التفسيرات الناتجة تظهر درجات بلو تنافسية ضد تفسيرات الحقيقة الأرضية، إلا أنها تفشل في تحسين أداء التنبؤ.نوضح مزيد من التفسيرات التي تم إنشاؤها في كثير من الأحيان معلومات الهلوسة والأيس عن العناصر الرئيسية التي تشير إلى الملصق.
Although neural models have shown strong performance in datasets such as SNLI, they lack the ability to generalize out-of-distribution (OOD). In this work, we formulate a few-shot learning setup and examine the effects of natural language explanations on OOD generalization. We leverage the templates in the HANS dataset and construct templated natural language explanations for each template. Although generated explanations show competitive BLEU scores against ground truth explanations, they fail to improve prediction performance. We further show that generated explanations often hallucinate information and miss key elements that indicate the label.
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An emerging line of research in Explainable NLP is the creation of datasets enriched with human-annotated explanations and rationales, used to build and evaluate models with step-wise inference and explanation generation capabilities. While human-ann
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