Search Results for author: Chris DuBois

Found 4 papers, 1 papers with code

Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks

no code implementations COLING 2022 Rajiv Movva, Jinhao Lei, Shayne Longpre, Ajay Gupta, Chris DuBois

Our work quantitatively demonstrates that combining compression methods can synergistically reduce model size, and that practitioners should prioritize (1) quantization, (2) knowledge distillation, and (3) pruning to maximize accuracy vs. model size tradeoffs.

Knowledge Distillation Neural Network Compression +1

Entity-Based Knowledge Conflicts in Question Answering

1 code implementation EMNLP 2021 Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, Sameer Singh

To understand how models use these sources together, we formalize the problem of knowledge conflicts, where the contextual information contradicts the learned information.

Hallucination Out-of-Distribution Generalization +1

An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering

no code implementations WS 2019 Shayne Longpre, Yi Lu, Zhucheng Tu, Chris DuBois

To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation.

Data Augmentation Question Answering +2

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