natural-language-processing-3

Sentence-Pair Classification

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Libraries

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Datasets

DACCORD

RTE3-FR

GQNLI-FR

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Most implemented papers

New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French

R. Moot, Maximos Skandalis, Christian Retoré, Simon Robillard•Sun Dec 31 2023

DACCORD is introduced, an original dataset in French for automatic detection of contradictions between sentences and new, manually translated versions of two datasets, namely the well known dataset RTE3 and the recent dataset GQNLI, from English to French, for the task of natural language inference / recognising textual entailment.

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Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

Xipeng Qiu, Luyao Huang, Chi Sun•Thu Mar 21 2019

This paper constructs an auxiliary sentence from the aspect and converts ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI), and fine-tune the pre-trained model from BERT.

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CLUE: A Chinese Language Understanding Evaluation Benchmark

Junyi Li, Kai Sun, Dian Yu, Kyle Richardson, Xuanwei Zhang, Qianqian Dong, Yiming Cui, Lu Li, Hai Hu, Chenjie Cao, Weitang Liu, Yudong Li, Yechen Xu, Cong Yu, Yin Tian, Bo Shi, Jun-jie Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhen-Yi Yang, Zhenzhong Lan•Sun Apr 12 2020

The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is introduced, an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text.

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Glyce: Glyph-vectors for Chinese Character Representations

Fei Wang, Yuxian Meng, Xiaofei Sun, Jiwei Li, Qinghong Han, Xiaoya Li, Wei Wu, Muyu Li, J. Mei, Ping Nie•Mon Jan 28 2019

Glyce, the glyph-vectors for Chinese character representations, is presented and it is shown that glyph-based models are able to consistently outperform word/char ID- based models in a wide range of Chinese NLP tasks.

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CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

Qingcai Chen, Luo Si, Ningyu Zhang, Shumin Deng, Zhen Bi, Xiaozhuan Liang, Xiang Chen, Luoqiu Li, Hongbin Ye, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Mosha Chen, Fei Huang, Yuan Ni, G. Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Huajun Chen, Buzhou Tang•Mon Jun 14 2021

The first Chinese Biomedical Language Understanding Evaluation Evaluation (CBLUE) benchmark is presented: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis.

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Continual and Multi-Task Architecture Search

Mohit Bansal, Ramakanth Pasunuru•Tue Jun 11 2019

This work introduces a novel continual architecture search (CAS) approach, so as to continually evolve the model parameters during the sequential training of several tasks, without losing performance on previously learned tasks, thus enabling life-long learning.

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Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach

Yue Yu, Haoming Jiang, T. Zhao, Chao Zhang, Simiao Zuo, Wendi Ren•Wed Oct 14 2020

This work develops a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision, underpinned by contrastive regularization and confidence-based reweighting, which gradually improves model fitting while effectively suppressing error propagation.

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Elastic weight consolidation for better bias inoculation

Andreas Vlachos, James Thorne•Thu Dec 31 2020

It is shown that elastic weight consolidation (EWC) allows fine-tuning of models to mitigate biases while being less susceptible to catastrophic forgetting, yielding models with lower levels of forgetting on the original dataset for equivalent gains in accuracy on the fine- Tuning (unbiased) dataset.

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Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

Xu Sun, Bin He, Lei Li, Zhiyuan Zhang, Xuancheng Ren, Wenkai Yang•Sun Mar 28 2021

This paper finds that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples.

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