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1
Geographic Adaptation of Pretrained Language Models ...
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2
Data for paper: "Evaluating Resource-Lean Cross-Lingual Embedding Models in Unsupervised Retrieval" ...
Litschko, Robert; Glavaš, Goran. - : Mannheim University Library, 2021
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3
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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4
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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5
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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6
AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings ...
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7
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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8
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ...
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9
Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer ...
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10
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ...
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11
Probing Pretrained Language Models for Lexical Semantics ...
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12
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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13
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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14
Informing unsupervised pretraining with external linguistic knowledge
Lauscher, Anne; Vulić, Ivan; Ponti, Edoardo Maria. - : Cornell University, 2019
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15
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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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16
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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17
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ...
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