Papers with supervised MT

2 papers
Flow-Adapter Architecture for Unsupervised Machine Translation (2022.acl-long)

Copied to clipboard

Challenge: Recent advances in deep learning have boosted the development of neural machine translation (NMT).
Approach: They propose a flow-adapter architecture for unsupervised neural machine translation that leverages normalizing flows to model distributions of sentence-level latent representations.
Outcome: The proposed model achieves competitive results on several unsupervised MT benchmarks.
Unsupervised Paraphrasing without Translation (P19-1)

Copied to clipboard

Challenge: Recent work on automatic paraphrasing focuses on methods leveraging machine translation as an intermediate step.
Approach: They propose to learn paraphrasing models only from a monolingual corpus . they propose a residual variant of vector-quantized variational auto-encoder .
Outcome: The proposed model outperforms supervised and unsupervised translation methods in paraphrase identification and training set augmentation.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations