Learning Semantic Sentence Embeddings using Pair-wise Discriminator
Table 2: Analysis of Baselines and State-of-the-Art methods for paraphrase generation on Quora dataset. As we can see clearly that our model outperforms the state-of-the-art methods by a significant margin in terms of BLEU and TER scores. Detailed analysis is present in section 4.1.4. A lower TER score is better whereas for the other metrics, a higher score is better. Also more details for the metrics are present in the supplementary material.
['[BOLD] Dataset', '[BOLD] Model', '[BOLD] BLEU1', '[BOLD] METEOR', '[BOLD] TER']
There has been relatively lesser work on this dataset and the only work which we came across was that of [gupta_AAAI2017]. We further compare our method EDD Full with their VAE-SVG-eq which is the current state-of-the-art on Quora datset. As we can see from the table that we achieve a significant increase of 24% in BLEU score and 11% in TER score for 50K dataset and similarly 22% in BLEU score and 7.5% in TER score for 100K dataset.