Search Results for author: Libin Shen

Found 9 papers, 2 papers with code

Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks

no code implementations EMNLP 2020 Chengyue Jiang, Yinggong Zhao, Shanbo Chu, Libin Shen, Kewei Tu

On the other hand, symbolic rules such as regular expressions are interpretable, require no training, and often achieve decent accuracy; but rules cannot benefit from labeled data when available and hence underperform neural networks in rich-resource scenarios.

text-classification Text Classification

What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension

no code implementations Findings (EMNLP) 2021 Jiawei Wang, Hai Zhao, Yinggong Zhao, Libin Shen

Machine reading comprehension (MRC) is a challenging NLP task for it requires to carefully deal with all linguistic granularities from word, sentence to passage.

Chinese Reading Comprehension Machine Reading Comprehension +1

Lite Unified Modeling for Discriminative Reading Comprehension

1 code implementation ACL 2022 Yilin Zhao, Hai Zhao, Libin Shen, Yinggong Zhao

As a broad and major category in machine reading comprehension (MRC), the generalized goal of discriminative MRC is answer prediction from the given materials.

Machine Reading Comprehension Multi-Choice MRC +1

Learning Numeral Embedding

1 code implementation Findings of the Association for Computational Linguistics 2020 Chengyue Jiang, Zhonglin Nian, Kaihao Guo, Shanbo Chu, Yinggong Zhao, Libin Shen, Kewei Tu

Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.

Word Similarity

MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data

no code implementations26 Apr 2020 Xiaoqing Geng, Xiwen Chen, Kenny Q. Zhu, Libin Shen, Yinggong Zhao

In this framework, models not only strive to classify query instances, but also seek underlying knowledge about the support instances to obtain better instance representations.

Classification Few-Shot Relation Classification +2

Learning Numeral Embeddings

no code implementations28 Dec 2019 Chengyue Jiang, Zhonglin Nian, Kaihao Guo, Shanbo Chu, Yinggong Zhao, Libin Shen, Kewei Tu

Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.

Word Similarity

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