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Don't Read Too Much into It: Adaptive Computation for Open-Domain
  Question Answering

Don't Read Too Much into It: Adaptive Computation for Open-Domain Question Answering

10 November 2020
Yuxiang Wu
Sebastian Riedel
Pasquale Minervini
Pontus Stenetorp
ArXiv (abs)PDFHTML

Papers citing "Don't Read Too Much into It: Adaptive Computation for Open-Domain Question Answering"

8 / 8 papers shown
FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction
FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction
Akriti Jain
Saransh Sharma
Koyel Mukherjee
Soumyabrata Pal
427
0
0
16 Oct 2024
FastFiD: Improve Inference Efficiency of Open Domain Question Answering
  via Sentence Selection
FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence SelectionAnnual Meeting of the Association for Computational Linguistics (ACL), 2024
Yufei Huang
Xu Han
Maosong Sun
308
3
0
12 Aug 2024
MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge
  Sources
MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge SourcesAnnual Meeting of the Association for Computational Linguistics (ACL), 2024
Dongkyu Lee
Chandana Satya Prakash
Jack G. M. FitzGerald
Jens Lehmann
RALM
274
4
0
07 Jun 2024
A Survey for Efficient Open Domain Question Answering
A Survey for Efficient Open Domain Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL), 2022
Qin Zhang
Shan Chen
Dongkuan Xu
Qingqing Cao
Xiaojun Chen
Trevor Cohn
Meng Fang
229
41
0
15 Nov 2022
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP
  Tasks
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP TasksConference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Yuxiang Wu
Yu Zhao
Baotian Hu
Pasquale Minervini
Pontus Stenetorp
Sebastian Riedel
RALMKELM
265
58
0
30 Oct 2022
Training Adaptive Computation for Open-Domain Question Answering with
  Computational Constraints
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints
Yuxiang Wu
Pasquale Minervini
Pontus Stenetorp
Sebastian Riedel
260
5
0
05 Jul 2021
Set-to-Sequence Methods in Machine Learning: a Review
Set-to-Sequence Methods in Machine Learning: a ReviewJournal of Artificial Intelligence Research (JAIR), 2021
Mateusz Jurewicz
Leon Derczynski
BDL
256
12
0
17 Mar 2021
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question
  Answering
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question AnsweringInternational Conference on Learning Representations (ICLR), 2019
Akari Asai
Kazuma Hashimoto
Hannaneh Hajishirzi
R. Socher
Caiming Xiong
RALMKELMLRM
648
319
0
24 Nov 2019
1
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