Pith. sign in

REVIEW 1 cited by

A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1910.00373 v1 pith:CS4P6UYG submitted 2019-10-01 cs.CL

Ilshat Gibadullin , Aidar Valeev , Albina Khusainova , Adil Khan This is my paper · ORCID
classification cs.CL
keywords machinetranslationdatalearninglow-resourcemethodsmonolingualneural
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Neural machine translation has become the state-of-the-art for language pairs with large parallel corpora. However, the quality of machine translation for low-resource languages leaves much to be desired. There are several approaches to mitigate this problem, such as transfer learning, semi-supervised and unsupervised learning techniques. In this paper, we review the existing methods, where the main idea is to exploit the power of monolingual data, which, compared to parallel, is usually easier to obtain and significantly greater in amount.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Language verY Rare for All

    cs.CL 2024-12 conditional novelty 5.0 of 10

    A single-GPU pipeline mixing LLM fine-tuning, RAG, and French-Italian transfer learning produces a French-Monégasque translator that matches or exceeds NLLB-200 on BLEU and METEOR.

Pith tools