DE eng

Search in the Catalogues and Directories

Hits 1 – 12 of 12

1
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
BASE
Show details
2
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
BASE
Show details
3
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
BASE
Show details
4
SemEval-2020 Task 3: Graded Word Similarity in Context ...
BASE
Show details
5
Emergent Communication Pretraining for Few-Shot Machine Translation ...
BASE
Show details
6
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
BASE
Show details
7
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
Abstract: Lexical entailment (LE) is a fundamental asymmetric lexico-semantic relation, supporting the hierarchies in lexical resources (e.g., WordNet, ConceptNet) and applications like natural language inference and taxonomy induction. Multilingual and cross-lingual NLP applications warrant models for LE detection that go beyond language boundaries. As part of SemEval 2020, we carried out a shared task (Task 2) on multilingual and cross-lingual LE. The shared task spans three dimensions: (1) monolingual LE in multiple languages versus cross-lingual LE, (2) binary versus graded LE, and (3) a set of 6 diverse languages (and 15 corresponding language pairs). We offered two different evaluation tracks: (a) distributional (Dist): for unsupervised, fully distributional models that capture LE solely on the basis of unannotated corpora, and (b) Any: for externally informed models, allowed to leverage any resources, including lexico-semantic networks (e.g., WordNet or BabelNet). In the Any track, we received system runs that ...
Keyword: Computer and Information Science; Natural Language Processing; Neural Network
URL: https://dx.doi.org/10.48448/2p49-kh89
https://underline.io/lecture/6409-semeval-2020-task-2-predicting-multilingual-and-cross-lingual-(graded)-lexical-entailment
BASE
Hide details
8
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
BASE
Show details
9
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
BASE
Show details
10
A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018
BASE
Show details
11
A deep learning approach to bilingual lexicon induction in the biomedical domain.
Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018
BASE
Show details
12
Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.
Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan. - : BioMed Central, 2018. : BMC bioinformatics, 2018
BASE
Show details

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
12
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern