SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding
Table 2. Evaluation on QQP dataset and MRPC.
['[BOLD] Model', 'QQP dataset Acc', 'QQP dataset F1', 'MRPC Acc', 'MRPC F1']
First, it’s obvious that general-purpose methods could not achieve satisfying performance compared with DML methods, which reveals that general-purpose embedding fail to generalize to paraphrase identification task and it is necessary to derive task-specific sentence embeddings. Second, no matter which encoder is used, SentPWNet increases the accuracy and f1-score by average 1% on QQP dataset and MSRP compared with other DML methods. It tells us that the locality weighting scheme is more effective than equal weighting ways. Furthermore, despite the fact that the independent pair mining stage such as hard sample mining do sample many informative pairs, our method can still mine more informative pairs during a single weighting stage to achieve better performance. In conclusion, our method is effective for paraphrase identification task.