Learning Semantic Sentence Embeddings using Pair-wise Discriminator
Table 1: Analysis of variants of our proposed method on Quora Dataset as mentioned in section 4.1.3 As we can see that our proposed method EDD Full clearly outperforms the other ablations on all metrics and detailed analysis is present in section 4.1.3.
['[BOLD] Dataset', '[BOLD] Model', '[BOLD] BLEU1', '[BOLD] BLEU2', '[BOLD] BLEU3', '[BOLD] BLEU4', '[BOLD] ROUGE', '[BOLD] METEOR']
We start with baseline model which we take as a simple encoder and decoder network (ED) [Sutskever_NIPS2014]. Further we have experimented with encoder-decoder and a discriminator network (EDD) to distinguish the ground truth paraphrase with predicted one. This method has only the global loss and not the local loss and the discriminator is the same as our proposed method, only the weight sharing is absent in this case. Another variation of our model is EDGAN, which has a discriminator and is trained alternately between local and global loss similar to GAN training. Finally, we make the discriminator share weights with the encoder and train this network with both the losses (EDD Full). Among the ablations, the proposed EDD Full method works way better than the other variants in terms of BLEU and METEOR metrics by achieving an improvement of 8% and 6% in the scores respectively over the baseline method for 50K dataset and an improvement of 10% and 7% in the scores respectively for 100K dataset.