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PL-Transformer: a POS-aware and layer ensemble transformer for text classification

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Abstract

The transformer-based models have become the de-facto standard for natural language processing (NLP) tasks. However, most of these models are only designed to capture the implicit semantics among tokens without considering the extra off-the-shelf knowledge (e.g., parts-of-speech) to facilitate the NLP tasks. Additionally, despite using multiple attention-based encoders, they only utilize the embeddings from the last layer, ignoring that from other layers. To address these issues, in this paper, we propose a novel POS-aware and layer ensemble transformer neural network (named as PL-Transformer). PL-Transformer utilizes the parts-of-speech information explicitly and leverages the outputs from different encoder layers with correlation coefficient attention (C-Encoder) jointly. Moreover, we use correlation coefficient attention to bound dot product in C-Encoder, which improves the overall model performance. Extensive experiments on four datasets demonstrate that PL-Transformer can improve the text classification performance. For example, the accuracy on the MPQA dataset is improved by 3.95% over the vanilla transformer.

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Notes

  1. https://github.com/interpretml/interpret-text.

  2. https://github.com/hankcs/pyhanlp.

  3. http://www.nltk.org/.

  4. https://github.com/Embedding/Chinese-Word-Vectors.

  5. https://code.google.com/archive/p/word2vec/.

  6. https://huggingface.co/bert-base-uncased.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (No. 61976026) and the 111 Project (B18008)

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  1. Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China

    Yu Shi, Xi Zhang & Ning Yu

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  1. Yu Shi
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  2. Xi Zhang
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Correspondence to Xi Zhang.

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Shi, Y., Zhang, X. & Yu, N. PL-Transformer: a POS-aware and layer ensemble transformer for text classification. Neural Comput & Applic 35, 1971–1982 (2023). https://doi.org/10.1007/s00521-022-07872-4

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