Question Dependent Recurrent Entity Network for Question Answering
Question Answering is a task which requires building models capable of providing answers to questions expressed in human language. Full question answering involves some form of reasoning ability. We introduce a neural network architecture for this task, which is a form of Memory Network, that recogn...
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| 出版年: | URI:https://journals.openedition.org/ijcol, |
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| 主要な著者: | , |
| フォーマット: | Article ou chapitre numérique |
| 言語: | Anglais |
| 出版事項: |
IJCoL
2020
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| オンライン･アクセス: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| 要約: | Question Answering is a task which requires building models capable of providing answers to questions expressed in human language. Full question answering involves some form of reasoning ability. We introduce a neural network architecture for this task, which is a form of Memory Network, that recognizes entities and their relations to answers through a focus attention mechanism. Our model is named Question Dependent Recurrent Entity Network and extends the Recurrent Entity Network by exploiting aspects of the question during the memorization process. We validate the model on both synthetic and real datasets: the bAbI question answering dataset and the CNN & Daily News reading comprehension dataset. In our experiments, our models improved the existing Recurrent Entity Network and achieved competitive results in both dataset. |
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