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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 ...
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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.410 Abstract: Transformer-based language models (LMs) pretrained on large text collections implicitly store a wealth of lexical semantic knowledge, but it is non-trivial to extract that knowledge effectively from their parameters. Inspired by prior work on semantic specialization of static word embedding (WE) models, we show that it is possible to expose and enrich lexical knowledge from the LMs, that is, to specialize them to serve as effective and universal "decontextualized" word encoders even when fed input words "in isolation" (i.e., without any context). Their transformation into such word encoders is achieved through a simple and efficient lexical fine-tuning procedure (termed LexFit) based on dual-encoder network structures. Further, we show that LexFit can yield effective word encoders even with limited lexical supervision and, via cross-lingual transfer, in different languages without any readily available external knowledge. Our evaluation ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/2skf-gv34
https://underline.io/lecture/25829-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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