A study of neural matching models for cross-lingual IR
Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020•dl.acm.org
In this study, we investigate interaction-based neural matching models for ad-hoc cross-
lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs). With
experiments conducted on the CLEF collection over four language pairs, we evaluate and
provide insight into different neural model architectures, different ways to represent query-
document interactions and word-pair similarity distributions in CLIR. This study paves the
way for learning an end-to-end CLIR system using CLWEs.
lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs). With
experiments conducted on the CLEF collection over four language pairs, we evaluate and
provide insight into different neural model architectures, different ways to represent query-
document interactions and word-pair similarity distributions in CLIR. This study paves the
way for learning an end-to-end CLIR system using CLWEs.
In this study, we investigate interaction-based neural matching models for ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs). With experiments conducted on the CLEF collection over four language pairs, we evaluate and provide insight into different neural model architectures, different ways to represent query-document interactions and word-pair similarity distributions in CLIR. This study paves the way for learning an end-to-end CLIR system using CLWEs.
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