简介：ParaBLEU: Generative Pretraining for Paraphrase Evaluation
ParaBLEU: Generative Pretraining for Paraphrase Evaluation
With the increasing interest in natural language processing (NLP), the task of evaluating the similarity between two sentences has gained significant attention. Paraphrasing, which involves expressing the same idea in different words, is a crucial aspect of this evaluation. Here, we present ParaBLEU, a generative pretraining approach for paraphrase evaluation.
ParaBLEU is based on a pretrained language model, which is fine-tuned to generate a target sentence given its paraphrase. The pretraining process occurs in an unsupervised manner, requiring only a large corpus of text. The key to ParaBLEU’s effectiveness is the use of the BLEU score—a metric commonly used in NLP to evaluate the similarity between two sentences—as the training objective.
During pretraining, ParaBLEU takes as input a source sentence and its paraphrase, and aims to maximize the BLEU score between the generated sentence and the target sentence. This process occurs in an iterative manner, with the language model gradually learning to represent the paraphrase in its encoded form. ParaBLEU’s pretraining process results in a highly tunable and expressive language model that can generate a wide variety of paraphrased sentences.
Once pretrained, ParaBLEU can be used to evaluate the similarity between two sentences by generating a target sentence from its paraphrase using the pretrained language model. The BLEU score is then computed between the generated sentence and the target sentence, quantifying their similarity. The advantage of ParaBLEU over traditional methods is its ability to capture global sentence-level information—including meaning, context, and grammar—through the pretraining process, enabling more accurate paraphrase evaluation.
In addition, ParaBLEU’s use of a pretrained language model provides two key benefits. First, it enables the model to generate diverse and natural-sounding sentences with minimum supervision, reducing the need for large labeled datasets for training. Second, ParaBLEU’s ability to encode language information into its pretrained model allows it to generalize well to unseen sentence pairs, even those involving novel domains or languages.
Experimental results on multiple datasets demonstrate the effectiveness of ParaBLEU for paraphrase evaluation. When compared with traditional methods, ParaBLEU consistently outperforms them across a range of evaluation metrics, including BLEU score, precision, recall, and F1-score. Furthermore, qualitative analysis of generated sentences shows that ParaBLEU is able to capture important aspects of sentence meaning and structure while generating natural-sounding sentences.
In summary, ParaBLEU is a highly effective generative pretraining approach for paraphrase evaluation that outperforms traditional methods while requiring minimum supervision and generalization capabilities. Its ability to capture global sentence-level information and generate diverse sentences makes it a valuable tool for NLP applications involving sentence similarity evaluation.