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., Nigel (2)
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Hits 1 – 7 of 7
1
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Nigel
;
Korhonen, Anna
. - : Underline Science Inc., 2021
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2
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Nigel
;
Korhonen, Anna
. - : Underline Science Inc., 2021
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3
Emergent Communication Pretraining for Few-Shot Machine Translation ...
The 28th International Conference on Computational Linguistics 2020
;
Korhonen, Anna
;
Li, Yaoyiran
. - : Underline Science Inc., 2020
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4
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
The 28th International Conference on Computational Linguistics 2020
;
Korhonen, Anna
;
Majewska, Olga
. - : Underline Science Inc., 2020
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5
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
The 28th International Conference on Computational Linguistics 2020
;
Glavaš, Goran
;
Korhonen, Anna
. - : Underline Science Inc., 2020
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6
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
The 28th International Conference on Computational Linguistics 2020
;
Glavaš, Goran
;
Korhonen, Anna
;
Lauscher, Anne
;
Ponti, Edoardo
;
Vulić, Ivan
. - : Underline Science Inc., 2020
Abstract:
Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through language modeling objectives. In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining. To this end, we generalize the standard BERT model to a multi-task learning setting where we couple BERT’s masked language modeling and next sentence prediction objectives with an auxiliary task of binary word relation classification. Our experiments suggest that our “Lexically Informed” BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind “vanilla” BERT on several language understanding tasks. Concretely, LIBERT ...
Keyword:
Computer and Information Science
;
Natural Language Processing
;
Neural Network
URL:
https://dx.doi.org/10.48448/j696-8h54
https://underline.io/lecture/6311-specializing-unsupervised-pretraining-models-for-word-level-semantic-similarity
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7
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Ponti, Edoardo,
;
O 'horan, Helen
;
Berzak, Yevgeni
...
In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
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