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1
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification ...
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2
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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3
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
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4
On cross-lingual retrieval with multilingual text encoders
Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer Science + Business Media, 2022
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5
SimLex-999 Slovenian translation SimLex-999-sl 1.0
Pollak, Senja; Vulić, Ivan; Pelicon, Andraž. - : University of Ljubljana, 2021
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6
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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7
Parameter space factorization for zero-shot learning across tasks and languages ...
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8
Analogy Training Multilingual Encoders ...
Garneau, Nicolas; Hartmann, Mareike; Sandholm, Anders. - : Apollo - University of Cambridge Repository, 2021
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9
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
Liu, Qianchu; Liu, Fangyu; Collier, Nigel. - : Apollo - University of Cambridge Repository, 2021
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10
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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11
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.243 Abstract: In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. We study a set of nine typologically diverse languages with readily available pretrained monolingual models on a set of five diverse monolingual downstream tasks. We first aim to establish, via fair and controlled comparisons, if a gap between the multilingual and the corresponding monolingual representation of that language exists, and subsequently investigate the reason for any performance difference. To disentangle conflating factors, we train new monolingual models on the same data, with monolingually and multilingually trained tokenizers. We find that while the pretraining data size is an important factor, a designated monolingual tokenizer plays an equally important role in the downstream performance. Our results show that ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25575-how-good-is-your-tokenizerquestion-on-the-monolingual-performance-of-multilingual-language-models
https://dx.doi.org/10.48448/bdjq-kw21
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12
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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13
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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14
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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15
Semantic Data Set Construction from Human Clustering and Spatial Arrangement ...
Majewska, Olga; McCarthy, Diana; Van Den Bosch, Jasper JF. - : Apollo - University of Cambridge Repository, 2021
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16
Parameter space factorization for zero-shot learning across tasks and languages
In: Transactions of the Association for Computational Linguistics, 9 (2021)
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17
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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18
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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19
LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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20
Verb Knowledge Injection for Multilingual Event Processing ...
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