REVIEW 1 cited by
Multi-Domain Neural Machine Translation
Not yet reviewed by Pith; the record is open.
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
read the original abstract
We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domains as distinct languages and use multilingual NMT methods to create multi-domain translation systems, we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of pre-specified text domains is necessary, turns out that it is after all, but also that when it is not known quite high translation quality can be reached.
Forward citations
Cited by 1 Pith paper
-
SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters
Sharing one MLP layer's weights across several layers plus low-rank adapters recovers most of a pretrained LLM's quality with a fraction of the storage and faster phone inference.
Discussion (0). Continue with ORCID to comment.