Papers with RULEC-GEC

2 papers
Grammatical Error Correction via Sequence Tagging for Russian (2025.acl-srw)

Copied to clipboard

Challenge: Several types of models have been suggested for grammatical error correction . despite being successful, the difference between GEC and machine translation is not taken into account .
Approach: They propose a modified sequence tagging architecture for the Russian language to be used for grammatical error correction.
Outcome: The proposed model outperforms previous approaches on two Russian GEC benchmarks while achieving competitive performance on RULEC-GEC.
Universal Dependencies for Learner Russian (2024.lrec-main)

Copied to clipboard

Challenge: a pilot study of Russian learner data with syntactic dependency relations is presented . a focus of recent work in the NLP community has been on grammar errors .
Approach: They propose to annotate Russian learner data with syntactic dependency relations using a subset of sentences from two error-corrected Russian learners.
Outcome: The proposed annotations are performed on a subset of Russian learner datasets.

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