Improving Domain-Specific Retrieval by NLI Fine-Tuning

• URL: http://arxiv.org/abs/2308.03103v1
• Date: Sun, 6 Aug 2023 12:40:58 GMT
• Title: Improving Domain-Specific Retrieval by NLI Fine-Tuning
• Authors: Roman Du\v{s}ek, Aleksander Wawer, Christopher Galias, Lidia Wojciechowska
• Abstract summary: This article investigates the fine-tuning potential of natural language inference (NLI) data to improve information retrieval and ranking. 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.
• Score: 64.79760042717822
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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