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Register a new userQuestion answering in Natural Language: the Special Case of Temporal Expressions
الإجابة على اللغة الطبيعية: الحالة الخاصة للتعبيرات الزمنية
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على الرغم من أن الإجابة على الأسئلة العامة قد تم استكشافها جيدا في السنوات الأخيرة، فإن الإجابة السؤال الزمنية هي مهمة لم تتلق أكبر قدر ممكن من التركيز.يهدف عملنا إلى الاستفادة من نهج شعبي المستخدم للاستفادة العامة الإجابة، والإجابة على استخراج، من أجل العثور على إجابات للمسائل الزمنية في الفقرة.لتدريب نموذجنا، نقترح مجموعة بيانات جديدة، مستوحاة من الفريق، وهي سؤال من أحدث سؤال حول كوربوس، خصيصا خصيصا لتوفير معلومات زمنية غنية من خلال تكييف WikiWars، والتي تحتوي على العديد من الوثائق حول أعظم صراعات التاريخ.يوضح تقييمنا أن نموذج مطابق لنموذج التعلم العميق، وغالبا ما يستخدم في الإجابة على السؤال العام، يمكن تكييفه مع السؤال الزمني الرد، إذا قبلنا طرح الأسئلة التي يجب أن تكون إجاباتها موجودة مباشرة في النص.
Although general question answering has been well explored in recent years, temporal question answering is a task which has not received as much focus. Our work aims to leverage a popular approach used for general question answering, answer extraction, in order to find answers to temporal questions within a paragraph. To train our model, we propose a new dataset, inspired by SQuAD, a state-of-the-art question answering corpus, specifically tailored to provide rich temporal information by adapting the corpus WikiWars, which contains several documents on history's greatest conflicts. Our evaluation shows that a pattern matching deep learning model, often used in general question answering, can be adapted to temporal question answering, if we accept to ask questions whose answers must be directly present within a text.
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