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20192021
most citedA Study of Neural Matching Models for Cross-lingual IR

37 citations · 63 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.IR202310 cited

Soft Prompt Decoding for Multilingual Dense Retrieval

Zhiqi Huang, Hansi Zeng, Hamed Zamani +1

In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages. We demonstrate that applying state-of-the-…

cs.IR20231 cited

Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples

Ali Montazeralghaem, James Allan

Conversational recommender systems (CRSs) are improving rapidly, according to the standard recommendation accuracy metrics. However, it is essential to make sure that these systems…

cs.IR202126 cited

Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval

Zhiqi Huang, Hamed Bonab, Sheikh Muhammad Sarwar +2

Pretrained contextualized representations offer great success for many downstream tasks, including document ranking. The multilingual versions of such pretrained representations pr…

cs.IR202037 cited

A Study of Neural Matching Models for Cross-lingual IR

Puxuan Yu, James Allan

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 exper…

cs.IR2019

Semantic Driven Fielded Entity Retrieval

Shahrzad Naseri, Sheikh Muhammad Sarwar, James Allan

A common approach for knowledge-base entity search is to consider an entity as a document with multiple fields. Models that focus on matching query terms in different fields are po…