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1
Joint Verification and Reranking for Open Fact Checking Over Tables ...
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2
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.57 Abstract: Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems. However, the current AC approaches require tuning of all model parameters, and training state-of-the-art ODQA models requires significant computational resources that may not be available for most researchers. We propose Adaptive Passage Encoder, an AC method that can be applied to an existing ODQA model and can be trained efficiently on a single GPU. It keeps the parameters of the base ODQA model fixed, but it overrides the default layer-by-layer computation of the encoder with an AC policy that is trained to optimise the computational efficiency of the model. Our experimental results show that our method improves upon a state-of-the-art model on two datasets, and is also more accurate than previous AC methods due to the stronger base ODQA model. All source code and datasets are available at ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25573-training-adaptive-computation-for-open-domain-question-answering-with-computational-constraints
https://dx.doi.org/10.48448/kt10-g493
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3
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks ...
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4
Database reasoning over text ...
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5
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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6
A Simple Recipe for Multilingual Grammatical Error Correction ...
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7
Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models ...
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