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Register a new userIdentifying inherent disagreement in natural language inference
تحديد الخلاف الكامن في الاستدلال اللغوي الطبيعي
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إن الاستدلال اللغوي الطبيعي (NLI) هي مهمة تحديد ما إذا كان جزء من النص ينطوي أو يتناقض أو لا علاقة له بقطعة أخرى من النص.في هذه الورقة، نحقق في كيفية ندف الاستنتاجات المنهجية (أي، العناصر التي يتفق بها الناس على تسمية NLI) بصرف النظر عن عناصر الخلاف (أي عناصر، التي تؤدي إلى تشريحية مختلفة)، والتي تم تجاهلها معظم العمل السابق.لتمييز الاستدلالات المنهجية عن عناصر الخلاف، نقترح معلقين اصطناعي (AAS) لمحاكاة عدم اليقين في عملية التوضيحية من خلال التقاط الأوضاع في التعليقات التوضيحية.تؤكد النتائج على الالتزام، وهي وجعة من خطابات حدوث طبيعية باللغة الإنجليزية، أن نهجنا يؤدي إحصائيا أفضل بكثير من جميع خطوط الأساس.نوضح كذلك أن AAS تعلم الأنماط اللغوية والتفكير المعتمد على السياق.
Natural language inference (NLI) is the task of determining whether a piece of text is entailed, contradicted by or unrelated to another piece of text. In this paper, we investigate how to tease systematic inferences (i.e., items for which people agree on the NLI label) apart from disagreement items (i.e., items which lead to different annotations), which most prior work has overlooked. To distinguish systematic inferences from disagreement items, we propose Artificial Annotators (AAs) to simulate the uncertainty in the annotation process by capturing the modes in annotations. Results on the CommitmentBank, a corpus of naturally occurring discourses in English, confirm that our approach performs statistically significantly better than all baselines. We further show that AAs learn linguistic patterns and context-dependent reasoning.
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