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Register a new userDecontextualization: Making Sentences Stand-Alone
فك التشفير: صنع الجمل قائمة بذاتها
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غالبا ما تفسر النماذج التجريدية للمسألة، وكلاء الحوار، والتلخيص معنى الجملة في سياق غني واستخدام هذا المعنى في سياق جديد. يمكن أن يكون أخذ مقتطفات النص مشكلة، لأن القطع الرئيسية قد لا تكون واضحة في نافذة محلية. لقد عزلت وحدد مشكلة الحكم من الحكم: أخذ جملة جنبا إلى جنب مع سياقها وإعادة كتابة الأمر لتفسيره من السياق، مع الحفاظ على معناها. نحن نصف إجراء شرح، وجمع البيانات عن كوربوس ويكيبيديا، واستخدام البيانات لتدريب نماذج لإزالة الجمل تلقائيا. نقدم دراسات أولية توضح قيمة الجملة التضخم في مهمة مواجهة للمستخدم، وكما قبل معالجة النظم التي تقوم بتفهم المستند. نقول أن التثقيف هو متعة مهمة فرعية في العديد من التطبيقات المصبقة، وأن التعريفات والموارد المقدمة يمكن أن تستفيد المهام التي تعمل على الجمل التي تحدث في سياق أكثر ثراء.
Abstract Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user-facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.
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