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- W2799042618 abstract "While increasingly complex approaches to question answering (QA) have been proposed, the true gain of these systems, particularly with respect to their expensive training requirements, can be in- flated when they are not compared to adequate baselines. Here we propose an unsupervised, simple, and fast alignment and informa- tion retrieval baseline that incorporates two novel contributions: a one-to-many alignment between query and document terms and negative alignment as a proxy for discriminative information. Our approach not only outperforms all conventional baselines as well as many supervised recurrent neural networks, but also approaches the state of the art for supervised systems on three QA datasets. With only three hyperparameters, we achieve 47% [email protected] on an 8th grade Science QA dataset, 32.9% [email protected] on a Yahoo! answers QA dataset and 64% MAP on WikiQA." @default.
- W2799042618 created "2018-05-07" @default.
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- W2799042618 date "2018-06-27" @default.
- W2799042618 modified "2023-09-25" @default.
- W2799042618 title "Sanity Check" @default.
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- W2799042618 doi "https://doi.org/10.1145/3209978.3210142" @default.
- W2799042618 hasPublicationYear "2018" @default.
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