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Real-world text classification tasks often require many labeled training examples that are expensive to obtain.
Then, we show that the lower bound of such a separation rank can reveal the quantitative relation between the network structure (e. g. depth/width) and the modeling ability for the contextual dependency.
To accelerate inference and reduce model size while maintaining accuracy, we firstly propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models.
Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches.
Simile recognition is to detect simile sentences and to extract simile components, i. e., tenors and vehicles.
When combing with BERT, we are able to set new state-of-the-art results for a variety of Chinese NLP tasks, including language modeling, tagging (NER, CWS, POS), sentence pair classification (BQ, LCQMC, XNLI, NLPCC-DBQA), single sentence classification tasks (ChnSentiCorp, the Fudan corpus, iFeng), dependency parsing, and semantic role labeling.
However, evaluating a model's robustness to these changes is harder for language since words are discrete and an automated change (e. g. adding `noise') to a query sometimes changes the meaning and thus labels of a query.
In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies.
The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed.