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Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Verwendung von Wissensgraphen zur inhaltlichen Ergänzung kleinerer Textkorpora ...
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Verwendung von Wissensgraphen zur inhaltlichen Ergänzung kleinerer Textkorpora ...
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Semantische Suche mit Word Embeddings für ein mehrsprachiges Wörterbuchportal ...
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Semantische Suche mit Word Embeddings für ein mehrsprachiges Wörterbuchportal ...
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Semantische Suche mit Word Embeddings für ein mehrsprachiges Wörterbuchportal ...
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Semantische Suche mit Word Embeddings für ein mehrsprachiges Wörterbuchportal ...
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| 10 |
Measuring and Comparing Social Bias in Static and Contextual Word Embeddings
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In: Dissertations (2022)
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| 11 |
A Generative Model for Topic Discovery and Polysemy Embeddings on Directed Attributed Networks
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In: Symmetry; Volume 14; Issue 4; Pages: 703 (2022)
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| 12 |
A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information
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In: Sensors; Volume 22; Issue 3; Pages: 1066 (2022)
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| 13 |
Emotion and Reason in Political Language
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In: The Economic Journal, 132 (643) (2022)
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| 14 |
When Classifying Arguments, BERT Doesn't Care About Word Order. Except When It Matters
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 15 |
Tackling Morphological Analogies Using Deep Learning -- Extended Version
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In: https://hal.inria.fr/hal-03425776 ; 2021 (2021)
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| 16 |
About Neural Networks and Writing Definitions
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In: ISSN: 2160-5076 ; Dictionaries: Journal of the Dictionary Society of North America Dictionary Society of North America ; https://hal.archives-ouvertes.fr/hal-03547452 ; Dictionaries: Journal of the Dictionary Society of North America Dictionary Society of North America, 2021, 42 (2), ⟨10.1353/dic.2021.0022⟩ (2021)
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| 17 |
Models of diachronic semantic change using word embeddings ; Modèles diachroniques à base de plongements de mot pour l'analyse du changement sémantique
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In: https://tel.archives-ouvertes.fr/tel-03199801 ; Document and Text Processing. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG006⟩ (2021)
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| 18 |
Multilingual word embeddings and low resources: identifying influence in Antiquity
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In: JADH 2021 ; JADH 2021 “Digital Humanities and COVID-19” ; https://hal.archives-ouvertes.fr/hal-03340641 ; JADH 2021 “Digital Humanities and COVID-19”, Organizing Committee, Japanese Association for Digital Humanities, Sep 2021, Tokyo, Japan. pp.51-54 ; https://www.hi.u-tokyo.ac.jp/ (2021)
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| 19 |
Injecting Inductive Biases into Distributed Representations of Text ...
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Abstract:
Distributed real-valued vector representations of text (a.k.a. embeddings), learned by neural networks, encode various (linguistic) knowledge. To encode this knowledge into the embeddings the common approach is to train a large neural network on large corpora. There is, however, a growing concern regarding the sustainability and rationality of pursuing this approach further. We depart from the mainstream trend and instead, to incorporate the desired properties into embeddings, use inductive biases. First, we use Knowledge Graphs (KGs) as a data-based inductive bias to derive the semantic representation of words and sentences. The explicit semantics that is encoded in a structure of a KG allows us to acquire the semantic representations without the need of employing a large amount of text. We use graph embedding techniques to learn the semantic representation of words and the sequence-to-sequence model to learn the semantic representation of sentences. We demonstrate the efficacy of the inductive bias for ...
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Keyword:
Distributed Representations of Text; Inductive Biases; Knowledge Graphs; Sentence Embeddings; Variational Autoencoders; Word Embeddings
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URL: https://www.repository.cam.ac.uk/handle/1810/330972
https://dx.doi.org/10.17863/cam.78416
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