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∙ 05/24/2023

Mixture of Prompt Experts for Generalizable and Interpretable Question Answering

One of the ultimate quests of question answering (QA) is to deploy a sys...

0 Chenglei Si, et al. ∙

∙ 02/14/2023

READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises

For many real-world applications, the user-generated inputs usually cont...

0 Chenglei Si, et al. ∙

∙ 10/17/2022

Prompting GPT-3 To Be Reliable

Large language models (LLMs) show impressive abilities via few-shot prom...

0 Chenglei Si, et al. ∙

∙ 05/25/2022

Revisiting Calibration for Question Answering

Model calibration aims to adjust (calibrate) models' confidence so that ...

2 Chenglei Si, et al. ∙

∙ 12/20/2021

Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP

What are the units of text that we want to model? From bytes to multi-wo...

21 Sabrina J. Mielke, et al. ∙

∙ 09/11/2021

What's in a Name? Answer Equivalence For Open-Domain Question Answering

A flaw in QA evaluation is that annotations often only provide one gold ...

0 Chenglei Si, et al. ∙

∙ 06/08/2021

Adversarial Training for Machine Reading Comprehension with Virtual Embeddings

Adversarial training (AT) as a regularization method has proved its effe...

0 Ziqing Yang, et al. ∙

∙ 06/01/2021

SHUOWEN-JIEZI: Linguistically Informed Tokenizers For Chinese Language Model Pretraining

Conventional tokenization methods for Chinese pretrained language models...

0 Chenglei Si, et al. ∙

∙ 11/03/2020

CharBERT: Character-aware Pre-trained Language Model

Most pre-trained language models (PLMs) construct word representations a...

0 Wentao Ma, et al. ∙

∙ 04/29/2020

Benchmarking Robustness of Machine Reading Comprehension Models

Machine Reading Comprehension (MRC) is an important testbed for evaluati...

0 Chenglei Si, et al. ∙

∙ 10/28/2019

What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?

Multiple-Choice Reading Comprehension (MCRC) requires the model to read ...

0 Chenglei Si, et al. ∙

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