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1
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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2
Geographic Adaptation of Pretrained Language Models ...
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3
On cross-lingual retrieval with multilingual text encoders
Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer Science + Business Media, 2022
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4
XHate-999: analyzing and detecting abusive language across domains and languages
Glavaš, Goran [Verfasser]; Karan, Mladen [Verfasser]; Vulic, Ivan [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2021
DNB Subject Category Language
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5
Specializing unsupervised pretraining models for word-level semantic similarity
Lauscher, Anne [Verfasser]; Vulic, Ivan [Verfasser]; Ponti, Edoardo Maria [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2021
DNB Subject Category Language
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6
Towards instance-level parser selection for cross-lingual transfer of dependency parsers
Litschko, Robert [Verfasser]; Vulic, Ivan [Verfasser]; Agić, Želiko [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2021
DNB Subject Category Language
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7
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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8
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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9
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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10
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
Abstract: Pretrained multilingual text encoders based on neural Transformer architectures, such as multilingual BERT (mBERT) and XLM, have achieved strong performance on a myriad of language understanding tasks. Consequently, they have been adopted as a go-to paradigm for multilingual and cross-lingual representation learning and transfer, rendering cross-lingual word embeddings (CLWEs) effectively obsolete. However, questions remain to which extent this finding generalizes 1) to unsupervised settings and 2) for ad-hoc cross-lingual IR (CLIR) tasks. Therefore, in this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a large number of language pairs. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR -- a setup with no relevance judgments for IR-specific fine-tuning -- pretrained encoders fail to significantly outperform models ... : accepted at ECIR'21 (preprint) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; H.3.3; I.2.7; Information Retrieval cs.IR
URL: https://dx.doi.org/10.48550/arxiv.2101.08370
https://arxiv.org/abs/2101.08370
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11
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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12
LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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13
Verb Knowledge Injection for Multilingual Event Processing ...
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14
Is supervised syntactic parsing beneficial for language understanding tasks? An empirical investigation
Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2021
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15
Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
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16
Training and domain adaptation for supervised text segmentation
Glavaš, Goran; Ganesh, Ananya; Somasundaran, Swapna. - : Association for Computational Linguistics, 2021
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17
AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings ...
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18
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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19
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ...
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20
Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer ...
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