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 ...
|
|
|
|
BASE
|
|
Show 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 ...
|
|
|
|
Abstract:
A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. However, this ambition is largely hampered by the variation in structural and semantic properties, i.e. the typological profiles of the world's languages. In this work, we analyse the implications of this variation on the language modeling (LM) task. We present a large-scale study of state-of-the art n-gram based and neural language models on 50 typologically diverse languages covering a wide variety of morphological systems. Operating in the full vocabulary LM setup focused on word-level prediction, we demonstrate that a coarse typology of morphological systems is predictive of absolute LM performance. Moreover, fine-grained typological features such as exponence, flexivity, fusion, and inflectional synthesis are borne out to be responsible for the proliferation of low-frequency phenomena which are organically difficult to model by statistical architectures, or for the ... : ERC grant Lexical ...
|
|
URL: https://dx.doi.org/10.17863/cam.30216 https://www.repository.cam.ac.uk/handle/1810/282852
|
|
BASE
|
|
Hide 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
|
|
|
|