Encoder-Decoder model with local and pairwise loss along with shared encoder and discriminator network (EDLPS)

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of obtaining word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder. This discriminator is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validate our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and provide competitive results on the paraphrase generation and sentiment analysis task on standard dataset. These results are also shown to be statistically significant.

Github Link:https://github.com/dev-chauhan/PQG-pytorch.

2 The PQG dataset is available on this link: https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs.

3 website: https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs.

4 we report same baseline results as mentioned in [10]

5 website: www.kaggle.com/c/sentiment-analysis-on-movie-reviews.

6 Code: https://github.com/dev-chauhan/PQG-pytorch.