1 |
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Data for paper: "Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval" ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Data for paper: "Evaluating Resource-Lean Cross-Lingual Embedding Models in Unsupervised Retrieval" ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Data for paper: "Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval" ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Probing Pretrained Language Models for Lexical Semantics ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Probing Pretrained Language Models for Lexical Semantics ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers
|
|
Glavas, Goran; Agic, Zeljko; Vulic, Ivan. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.345, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
|
|
BASE
|
|
Show details
|
|
15 |
Towards instance-level parser selection for cross-lingual transfer of dependency parsers
|
|
|
|
BASE
|
|
Show details
|
|
16 |
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ...
|
|
|
|
Abstract:
Cross-lingual word embeddings (CLEs) enable multilingual modeling of meaning and facilitate cross-lingual transfer of NLP models. Despite their ubiquitous usage in downstream tasks, recent increasingly popular projection-based CLE models are almost exclusively evaluated on a single task only: bilingual lexicon induction (BLI). Even BLI evaluations vary greatly, hindering our ability to correctly interpret performance and properties of different CLE models. In this work, we make the first step towards a comprehensive evaluation of cross-lingual word embeddings. We thoroughly evaluate both supervised and unsupervised CLE models on a large number of language pairs in the BLI task and three downstream tasks, providing new insights concerning the ability of cutting-edge CLE models to support cross-lingual NLP. We empirically demonstrate that the performance of CLE models largely depends on the task at hand and that optimizing CLE models for BLI can result in deteriorated downstream performance. We indicate the ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/1902.00508 https://dx.doi.org/10.48550/arxiv.1902.00508
|
|
BASE
|
|
Hide details
|
|
17 |
How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only
|
|
|
|
BASE
|
|
Show details
|
|
|
|