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- W2893597360 abstract "With the growing amount of textual information in recent years, it has become quite challenging to keep up with content produced by humans. Many models have been proposed that can perform reading comprehension on a variety of texts; however, past models either excel at information retrieval on complex texts or inference on simple texts. In this paper, we propose a model called ALICE that can perform information retrieval as well as inference tasks on any text. It is scalable to any document size and can be used to aid professionals in quickly finding answers to their problems using natural language queries. We will explore how ALICE achieves this and test it on some common datasets." @default.
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- W2893597360 date "2018-01-01" @default.
- W2893597360 modified "2023-09-25" @default.
- W2893597360 title "ALICE: A Natural Language Question Answering System Using Dynamic Attention and Memory" @default.
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- W2893597360 doi "https://doi.org/10.1007/978-981-13-1936-5_30" @default.
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