Understanding the Representational Power of Neural Retrieval Models Using NLP Tasks-Reference-Cited by-同舟云学术

Understanding the Representational Power of Neural Retrieval Models Using NLP Tasks

Published:2018-09-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval
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Author:

Cohen Daniel¹,O'Connor Brendan¹,Croft W. Bruce¹

Affiliation:

1. University of Massachusetts Amherst, Amherst, MA, USA

Funder

University of Southern California

Air Force Research Laboratory

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3234944.3234959

Reference36 articles.

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2. Guillaume Alain and Yoshua Bengio . 2016. Understanding intermediate layers using linear classifier probes. CoRR Vol. abs/1610.01644 (2016). showeprint{arxiv}1610.01644 http://arxiv.org/abs/1610.01644 Guillaume Alain and Yoshua Bengio . 2016. Understanding intermediate layers using linear classifier probes. CoRR Vol. abs/1610.01644 (2016). showeprint{arxiv}1610.01644 http://arxiv.org/abs/1610.01644

3. Yonatan Belinkov Nadir Durrani Fahim Dalvi Hassan Sajjad and James R. Glass . 2017. What do Neural Machine Translation Models Learn about Morphology? ACL. 861--872. Yonatan Belinkov Nadir Durrani Fahim Dalvi Hassan Sajjad and James R. Glass . 2017. What do Neural Machine Translation Models Learn about Morphology? ACL. 861--872.

4. Samuel R. Bowman Gabor Angeli Christopher Potts and Christopher D. Manning . 2015. A large annotated corpus for learning natural language inference MNLP. ACL. Samuel R. Bowman Gabor Angeli Christopher Potts and Christopher D. Manning . 2015. A large annotated corpus for learning natural language inference MNLP. ACL.

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