Know When to Fuse: Investigating Non-English Hybrid Retrieval in the
  Legal Domain
Hybrid search has emerged as an effective strategy to offset the limitations
of different matching paradigms, especially in out-of-domain contexts where
notable improvements in retrieval quality have been observed. However, existing
research predominantly focuses on a limited set of retrieval methods, evaluated
in pairs on domain-general datasets exclusively in English. In this work, we
study the efficacy of hybrid search across a variety of prominent retrieval
models within the unexplored field of law in the French language, assessing
both zero-shot and in-domain scenarios. Our findings reveal that in a zero-shot
context, fusing different domain-general models consistently enhances
performance compared to using a standalone model, regardless of the fusion
method. Surprisingly, when models are trained in-domain, we find that fusion
generally diminishes performance relative to using the best single system,
unless fusing scores with carefully tuned weights. These novel insights, among
others, expand the applicability of prior findings across a new field and
language, and contribute to a deeper understanding of hybrid search in
non-English specialized domains.
66d8517dabb339ab61725c6a
Antoine Louis
claimed_verified
2024-09-04T15:25:00.048Z
5f298d07e923d665e6162802
https://cdn-avatars.huggingface.co/v1/production/uploads/1600354981837-5f298d07e923d665e6162802.jpeg
Antoine Louis
user
antoinelouis
66d8517dabb339ab61725c6b
Gijs van Dijck
66d8517dabb339ab61725c6c
Gerasimos Spanakis
Know When to Fuse: Investigating Non-English Hybrid Retrieval in the
  Legal Domain
Hybrid search has emerged as an effective strategy to offset the limitations
of different matching paradigms, especially in out-of-domain contexts where
notable improvements in retrieval quality have been observed. However, existing
research predominantly focuses on a limited set of retrieval methods, evaluated
in pairs on domain-general datasets exclusively in English. In this work, we
study the efficacy of hybrid search across a variety of prominent retrieval
models within the unexplored field of law in the French language, assessing
both zero-shot and in-domain scenarios. Our findings reveal that in a zero-shot
context, fusing different domain-general models consistently enhances
performance compared to using a standalone model, regardless of the fusion
method. Surprisingly, when models are trained in-domain, we find that fusion
generally diminishes performance relative to using the best single system,
unless fusing scores with carefully tuned weights. These novel insights, among
others, expand the applicability of prior findings across a new field and
language, and contribute to a deeper understanding of hybrid search in
non-English specialized domains.
Hybrid search improves retrieval quality in the French legal domain, especially in zero-shot scenarios, with tuned fusion methods enhancing performance in domain-specific settings.
hybrid search retrieval quality out-of-domain contexts domain-general datasets retrieval models zero-shot scenarios in-domain scenarios fusion methods performance enhancement tuned fusion methods