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Glavaš, Goran (8)
Vulić, Ivan (8)
Korhonen, Anna (3)
Litschko, Robert (3)
Ponzetto, Simone Paolo (3)
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 (3)
Majewska, Olga (2)
Maria Ponti, Edoardo (2)
Barikeri, Soumya (1)
Hiemstra, Djoerd (1)
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2021 (8)
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Simple Search
Hits 1 – 8 of 8
1
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
Razumovskaia, Evgeniia
;
Glavaš, Goran
;
Majewska, Olga
. - : arXiv, 2021
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2
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
Litschko, Robert
;
Vulić, Ivan
;
Ponzetto, Simone Paolo
. - : arXiv, 2021
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3
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
Litschko, Robert
;
Vulić, Ivan
;
Ponzetto, Simone Paolo
. - : arXiv, 2021
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4
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Barikeri, Soumya
;
Glavaš, Goran
. - : Underline Science Inc., 2021
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5
LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Glavaš, Goran
;
Korhonen, Anna
;
Maria Ponti, Edoardo
;
Vulić, Ivan
. - : Underline Science Inc., 2021
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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6
Verb Knowledge Injection for Multilingual Event Processing ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Glavaš, Goran
;
Korhonen, Anna
. - : Underline Science Inc., 2021
BASE
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7
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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8
Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
Litschko, Robert
;
Vulić, Ivan
;
Ponzetto, Simone Paolo
. - : Springer, 2021
BASE
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