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Revisiting paraphrase question generator using pairwise discriminator
- Source :
- Neurocomputing. 420:149-161
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing 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 that 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 validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.<br />Comment: This work is an extension of our COLING-2018 paper arXiv:1806.00807
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
0209 industrial biotechnology
Discriminator
Computer science
Cognitive Neuroscience
Context (language use)
02 engineering and technology
computer.software_genre
Paraphrase
Machine Learning (cs.LG)
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Computer Science - Computation and Language
business.industry
Sentiment analysis
Function (mathematics)
Computer Science Applications
020201 artificial intelligence & image processing
Pairwise comparison
Artificial intelligence
business
Computation and Language (cs.CL)
computer
Natural language processing
Sentence
Generator (mathematics)
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 420
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....6a5e2d0c8b3e60b0c8d010f7b73c35ed