Improving Domain-Specific Retrieval by NLI Fine-Tuning
Roman Dušek Aleksander Wawer Christopher Galias Lidia Wojciechowska
The aim of this article is to investigate the fine-tuning potential of
natural language inference (NLI) data to improve information retrieval and
ranking. We demonstrate this for both English and Polish languages, using data
from one of the largest Polish e-commerce sites and selected open-domain
datasets. We employ both monolingual and multilingual sentence encoders
fine-tuned by a supervised method utilizing contrastive loss and NLI data. Our
results point to the fact that NLI fine-tuning increases the performance of the
models in both tasks and both languages, with the potential to improve mono-
and multilingual models. Finally, we investigate uniformity and alignment of
the embeddings to explain the effect of NLI-based fine-tuning for an
out-of-domain use-case.