Simple and Effective Paraphrastic Similarity from Parallel Translations
arXiv:1909.13872
Abstract
We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating paraphrase corpora. Further, we show that the resulting model can be applied to cross-lingual tasks where it both outperforms and is orders of magnitude faster than more complex state-of-the-art baselines.
arXiv commentPublished as a short paper at ACL 2019
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
- Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
- An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification
- Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation
- Filtering and Mining Parallel Data in a Joint Multilingual Space