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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...
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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...
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10/17/2022
Prompting GPT-3 To Be Reliable
Large language models (LLMs) show impressive abilities via few-shot prom...
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05/25/2022
Revisiting Calibration for Question Answering
Model calibration aims to adjust (calibrate) models' confidence so that ...
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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...
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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 ...
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06/08/2021
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings
Adversarial training (AT) as a regularization method has proved its effe...
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06/01/2021
SHUOWEN-JIEZI: Linguistically Informed Tokenizers For Chinese Language Model Pretraining
Conventional tokenization methods for Chinese pretrained language models...
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11/03/2020
CharBERT: Character-aware Pre-trained Language Model
Most pre-trained language models (PLMs) construct word representations a...
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04/29/2020
Benchmarking Robustness of Machine Reading Comprehension Models
Machine Reading Comprehension (MRC) is an important testbed for evaluati...
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10/28/2019
