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1
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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2
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
Litschko, Robert; Glavas, Goran; Ponzetto, Simone Paolo. - : Apollo - University of Cambridge Repository, 2018
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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4
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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5
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ...
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6
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
Vulic, Ivan; Glavaš, Goran; Mrkšić, Nikola. - : Apollo - University of Cambridge Repository, 2018
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7
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only
Litschko, Robert; Glavas, Goran; Ponzetto, Simone Paolo; Vulic, Ivan; SIGIR, ACM. - : ACM, 2018. : ACM/SIGIR PROCEEDINGS 2018, 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 first step towards development of effective CLIR models for language pairs and domains where parallel data are scarce or non-existent.
Keyword: cross-lingual vector spaces; Unsupervised cross-lingual IR
URL: https://www.repository.cam.ac.uk/handle/1810/279400
https://doi.org/10.17863/CAM.26775
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8
ArguminSci: a tool for analyzing argumentation and rhetorical aspects in scientific writing
Glavaš, Goran; Lauscher, Anne; Eckert, Kai. - : Association for Computational Linguistics, 2018
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9
An argument-annotated corpus of scientific publications
Ponzetto, Simone Paolo; Lauscher, Anne; Glavaš, Goran. - : Association for Computational Linguistics, 2018
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10
Investigating the role of argumentation in the rhetorical analysis of scientific publications with neural multi-task learning models
Ponzetto, Simone Paolo; Eckert, Kai; Lauscher, Anne. - : Association for Computational Linguistics, 2018
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11
Post-specialisation: Retrofitting vectors of words unseen in lexical resources
Mrkšić, Nikola; Glavaš, Goran; Korhonen, Anna. - : Association for Computational Linguistics, 2018
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12
Discriminating between lexico-semantic relations with the specialization tensor model
Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018
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13
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018
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14
Explicit retrofitting of distributional word vectors
Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2018
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15
A resource-light method for cross-lingual semantic textual similarity
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