Research And Implementation Of Text Matching Techniques With Enhanced Semantic Representation

The rapid proliferation of the Internet has resulted in an exponential increase in textual data,which has presented significant challenges for natural language processing(NLP).The task of text matching is a fundamental component of NLP.In addition,text matching tasks in fewshot scenarios are becoming increasingly important as artificial intelligence evolves to accommodate specialized scenarios across a wide range of industries and to leverage the power of large-scale language models,and its accuracy and efficiency are crucial for many downstream applications such as information retrieval(IR),question answering(QA),and dialogue systems.With the advent of deep learning techniques and large-scale pretrained language models,there has been considerable progress in text matching.However,the pre-training tasks of these language models differ from the text matching task,and as such,their potential has not been fully realized.Prompt learning has emerged as a promising approach to address this limitation by enabling the introduction of explicit task-related information to enhance the representation of text.Nevertheless,current prompt-based text matching methods mainly rely on static templates,which do not account for the variability in instances.To overcome this limitation,this thesis proposes two novel prompt-based text matching approaches:(1)instance-guided prompt learning.(2)dynamic prompt learning.The former leverages a gating mechanism to enhance the adaptability of prompt templates to instances,while the latter transforms static prompt templates into dynamic templates by fusing semantic information of instances and explicit task-related information.Both methods aim to enhance the semantic representation of text using prompt learning.We evaluate the effectiveness of the proposed methods on five sentence pair datasets(MRPC,QQP,SNLI,QNLI and RTE)in few-shot scenarios,and the results demonstrate that the proposed approaches outperform the state-of-the-art models on all five datasets.Furthermore,we design and implement an integrated text matching system with our proposed model as the core,which provides stable and reliable services through Web interface and API services.The system testing confirms the stability and practicality of the proposed system.