1 |
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.
|
|
|
|
BASE
|
|
Show details
|
|
5 |
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Cross-lingual semantic specialization via lexical relation induction ...
|
|
|
|
Abstract:
Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints. However, this technique cannot be leveraged in many languages, because their structured external resources are typically incomplete or non-existent. To bridge this gap, we propose a novel method that transfers specialization from a resource-rich source language (English) to virtually any target language. Our specialization transfer comprises two crucial steps: 1) Inducing noisy constraints in the target language through automatic word translation; and 2) Filtering the noisy constraints via a state-of-the-art relation prediction model trained on the source language constraints. This allows us to specialize any set of distributional vectors in the target language with the refined constraints. We prove the effectiveness of our method through intrinsic word similarity evaluation in 8 languages, and with 3 downstream ...
|
|
URL: https://dx.doi.org/10.17863/cam.43734 https://www.repository.cam.ac.uk/handle/1810/296686
|
|
BASE
|
|
Hide details
|
|
7 |
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Do we really need fully unsupervised cross-lingual embeddings? ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Probing Pretrained Language Models for Lexical Semantics ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
On the relation between linguistic typology and (limitations of) multilingual language modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Spatial multi-arrangement for clustering and multi-way similarity dataset construction ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Cross-lingual semantic specialization via lexical relation induction
|
|
Ponti, Edoardo; Vulić, I; Glavaš, G. - : EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2020
|
|
BASE
|
|
Show details
|
|
16 |
On the relation between linguistic typology and (limitations of) multilingual language modeling
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
|
|
|
|
BASE
|
|
Show details
|
|
18 |
The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Spatial multi-arrangement for clustering and multi-way similarity dataset construction
|
|
Majewska, Olga; McCarthy, D; van den Bosch, J. - : European Language Resources Association, 2020. : LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, 2020
|
|
BASE
|
|
Show details
|
|
|
|