SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding
Table 4. The category distribution of POI-EM-CHN dataset
['[EMPTY]', 'sentence', 'title']
We use accuracy as the evaluation measure, the accurate prediction is decided by whether the similarity between positive and anchor is greater than the similarity between negative and anchor. Note that in both two datasets, all the DML methods work much better than the general-purpose embedding methods by 10% at least. For the sentence triplet dataset, because there are not enough negative samples, our model could not mine and weight informative pairs properly. So it’s reasonable that SentPWNet achieves almost same performance as Siamese network and Triplet Network. For the title triplet dataset, there are thousands of pairs for one anchor, SentPWNet can obtain more informative pairs during optimizing stage and weight them appropriately , therefore our model outperforms Siamese network and Triplet network by 2% above. The results demonstrate the effectiveness of locality weighting schema of SentPWNet. In conclusion, our model is also effective for thematic relatedness task.