Chinese text paraphrase recognition based on OpenPrompt introducing hybrid prompts

Chengbo Mao; Menglin Lu; Tongzhou Zhao

doi:10.1117/12.2640803

13 October 2022 Chinese text paraphrase recognition based on OpenPrompt introducing hybrid prompts

Chengbo Mao, Menglin Lu, Tongzhou Zhao

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871X (2022) https://doi.org/10.1117/12.2640803
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

The fine-tune paradigm adopted by traditional paraphrase recognition tasks cannot fully exploit the knowledge of pretrained language models (PLMs). At this stage, the Prompt paradigm reconstructs downstream tasks by constructing templates to make it more suitable for the training form of PLM. However, there are many processes, the code base is not supervised, and a single discrete template limits the model prediction ability. In response to this problem, this paper proposes a method of introducing hybrid prompts based on OpenPrompt. OpenPrompt makes the Prompt process have a unified framework, and hybrid prompts solve the problem that discrete templates cannot fully mine PLM knowledge. This paper first constructs a hybrid template with [mask] slots, and then transforms the original input through the template to obtain x_prompt , and then x_prompt is input to the bert-base-chinese model of the multi-layer bidirectional transformer based on the attention mechanism for training. When the model trains the optimal prompt, calculate the label with the highest probability of filling in the label set, and finally map the label to the prediction result. The experimental results show that compared with the fine-tune paradigm, the F1 value of the prompt paradigm exceeds 4.69% under the same PLM and hyperparameters. Compared with not using soft prompt, the average accuracy and average F1 value obtained with soft hints are 2.294% and 2.31% higher, respectively. Meanwhile, when the number of soft hints is 6, the accuracy and F1 value reach the highest.

Citation Download Citation

Chengbo Mao, Menglin Lu, and Tongzhou Zhao "Chinese text paraphrase recognition based on OpenPrompt introducing hybrid prompts", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871X (13 October 2022); https://doi.org/10.1117/12.2640803

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Transformers

Vector spaces

Analytical research

Computer programming

Machine learning

Optimization (mathematics)

Show All Keywords

Keywords/Phrases

Search In:

Publication Years