Qasar: Self-Supervised Learning Framework for Extractive Question Answering-Reference-Cited by-同舟云学术

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Qasar: Self-Supervised Learning Framework for Extractive Question Answering

Published:2021-12-15 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Big Data (Big Data)
language:
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Author:

Assem Haytham,Sarkar Rajdeep,Dutta Sourav

Funder

Irish Research Council

Science Foundation Ireland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9671263/9671273/09671570.pdf?arnumber=9671570

Reference39 articles.

1. Rethink Training of BERT Rerankers in Multi-stage Retrieval Pipeline

2. BM25;g;Encyclopedia of Database Systems,2009

3. A large annotated corpus for learning natural language inference

4. Glove: Global vectors for word representation;pennington;Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP),2014

5. On Measuring Social Biases in Sentence Encoders

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1. SESAME - self-supervised framework for extractive question answering over document collections;Journal of Intelligent Information Systems;2024-07-30

2. Cage: A Hybrid Framework for Closed-Domain Conversational Agents;Machine Learning and Knowledge Discovery in Databases;2023

3. Semantic Aware Answer Sentence Selection Using Self-Learning Based Domain Adaptation;Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2022-08-14

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