Papers by Varun Iyer

2 papers
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)

Copied to clipboard

Challenge: Paraphrase generation is a long-standing task in natural language processing (NLP).
Approach: They propose to generate large-scale syntactically diverse paraphrase datasets by abstract meaning representation back-translation.
Outcome: The proposed dataset is syntactically more diverse than existing datasets while maintaining good semantic similarity.
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations (2022.findings-emnlp)

Copied to clipboard

Challenge: Existing approaches to syntactically controlled paraphrase generation require annotated paraphrase pairs for training and are costly to extend to new domains.
Approach: They propose to leverage Abstract Meaning Representations (AMR) to improve the performance of unsupervised syntactically controlled paraphrase generation.
Outcome: The proposed model generates more accurate syntactically controlled paraphrases, both quantitatively and qualitatively, compared to the existing unsupervised approaches.

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