Extracting and filtering paraphrases by bridging natural language inference and paraphrasing
This work addresses the need for cleaner and more diverse paraphrase datasets for NLP tasks, though it is incremental as it builds on existing NLI and paraphrasing methods.
The paper tackled the problem of extracting and cleaning paraphrase datasets by using bidirectional entailment from natural language inference data, resulting in high-quality extracted datasets and revealing high noise levels in existing paraphrase datasets.
Paraphrasing is a useful natural language processing task that can contribute to more diverse generated or translated texts. Natural language inference (NLI) and paraphrasing share some similarities and can benefit from a joint approach. We propose a novel methodology for the extraction of paraphrasing datasets from NLI datasets and cleaning existing paraphrasing datasets. Our approach is based on bidirectional entailment; namely, if two sentences can be mutually entailed, they are paraphrases. We evaluate our approach using several large pretrained transformer language models in the monolingual and cross-lingual setting. The results show high quality of extracted paraphrasing datasets and surprisingly high noise levels in two existing paraphrasing datasets.