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Register a new userGenerating Answer Candidates for Quizzes and Answer-Aware Question Generators
توليد الإجابة المرشحين للحصول على مسابقات ومولدات استفسارات الإجابة
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في التعليم، أصبحت أسئلة الاختبار أداة مهمة لتقييم معرفة الطلاب.ومع ذلك، فإن إعداد هذه الأسئلة يدويا هو مهمة مملة، وبالتالي تم اقتراح توليد السؤال التلقائي كديل ممكن.حتى الآن، ركزت الغالبية العظمى من الأبحاث على توليد نص الأسئلة، والاعتماد على سؤال حول مجموعات البيانات مع الإجابات التي اختارها بسهولة، ومشكلة كيفية التوصل إلى إجابة المرشحين في المقام الأول تم تجاهلها إلى حد كبير.هنا، نحن نهدف إلى سد هذه الفجوة.على وجه الخصوص، نقترح نموذجا يمكن أن ينشئ عددا محددا من المرشحين للإجابة لمرق معين من النص، والذي يمكن بعد ذلك استخدامه من قبل المدربين لكتابة الأسئلة يدويا أو يمكن تمريرهم كمدخل لمولدات السؤال التلقائي للإجابة.تشير تجاربنا إلى أن نموذج جيل الرد الخاص بنا اقترح ينفأ على العديد من خطوط الأساس.
In education, quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far, the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored. Here, we aim to bridge this gap. In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators. Our experiments show that our proposed answer candidate generation model outperforms several baselines.
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The evaluation of question answering models compares ground-truth annotations with model predictions. However, as of today, this comparison is mostly lexical-based and therefore misses out on answers that have no lexical overlap but are still semanti
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Generating high quality question-answer pairs is a hard but meaningful task. Although previous works have achieved great results on answer-aware question generation, it is difficult to apply them into practical application in the education field. Thi
s paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE. To capture the important information of the input passage we first automatically generate (rather than extracting) keyphrases, thus this task is reduced to keyphrase-question-answer triplet joint generation. Accordingly, we propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively, and then apply the generated question and keyphrases to guide the generation of answers. To establish a solid benchmark, we build our model on the strong generative pre-training model. Experimental results show that our model makes great breakthroughs in the question-answer pair generation task. Moreover, we make a comprehensive analysis on our model, suggesting new directions for this challenging task.
Although showing promising values to downstream applications, generating question and answer together is under-explored. In this paper, we introduce a novel task that targets question-answer pair generation from visual images. It requires not only ge
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