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Hits 1 – 4 of 4
1
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
Litschko, Robert
;
Vulić, Ivan
;
Ponzetto, Simone Paolo
. - : arXiv, 2021
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2
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
Litschko, Robert
;
Vulić, Ivan
;
Ponzetto, Simone Paolo
. - : arXiv, 2021
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3
Probing Pretrained Language Models for Lexical Semantics ...
Vulić, Ivan
;
Ponti, Edoardo Maria
;
Litschko, Robert
. - : arXiv, 2020
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4
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
Litschko, Robert
;
Glavaš, Goran
;
Ponzetto, Simone Paolo
;
Vulić, Ivan
. - : arXiv, 2018
Abstract:
We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all. The framework leverages shared cross-lingual word embedding spaces in which terms, queries, and documents can be represented, irrespective of their actual language. The shared embedding spaces are induced solely on the basis of monolingual corpora in two languages through an iterative process based on adversarial neural networks. Our experiments on the standard CLEF CLIR collections for three language pairs of varying degrees of language similarity (English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed fully unsupervised approach. Our CLIR models with unsupervised cross-lingual embeddings outperform baselines that utilize cross-lingual embeddings induced relying on word-level and document-level alignments. We then demonstrate that further improvements can be achieved by unsupervised ensemble CLIR models. We believe that the proposed framework is the ... : accepted at SIGIR'18 (preprint) ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/1805.00879
https://dx.doi.org/10.48550/arxiv.1805.00879
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