A Study of Neural Matching Models for Cross-lingual IR
By Puxuan Yu, James Allan. 2020
27 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jimmy J. Lin
- Andrew Yates
- Rodrigo Nogueira
- Zeynep Akkalyoncu Yilmaz
- Sebastian Hofstätter
- Allan Hanbury
- Zhuyun Dai
- Jamie Callan
In Information Retrieval & Search
- Jimmy J. Lin
- Rodrigo Nogueira
- Andrew Yates
- Zeynep Akkalyoncu Yilmaz
- Sebastian Hofstätter
- Allan Hanbury
- Zhuyun Dai
- Wei Yang
In Natural Language Processing
- Jimmy J. Lin
- Rodrigo Nogueira
- Andrew Yates
- Zeynep Akkalyoncu Yilmaz
- Wei Yang
- Sheng-Chieh Lin
- Jheng-Hong Yang
- Haotian Zhang
Citations in the corpus
- Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “Although some of the early neural ranking approaches did explore cross-lingual retrieval [Vulić and Moens, 2015] and new research on this topic continues to emerge in the neural context [Yu and Allan, 2020], we have not found enough references in the context of transformers to provide a detailed tre”
- A Survey of Model Architectures in Information Retrieval (2025, unknown): “Interaction-based models with cross-lingual word embeddings (Joulin et al., 2018) for cross-lingual reranking have also been explored (Yu and Allan, 2020).”