41 |
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
|
|
|
|
BASE
|
|
Show details
|
|
42 |
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
|
|
|
|
BASE
|
|
Show details
|
|
43 |
Verb Knowledge Injection for Multilingual Event Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
44 |
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
45 |
Probing Pretrained Language Models for Lexical Semantics ...
|
|
|
|
BASE
|
|
Show details
|
|
46 |
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
|
|
|
|
BASE
|
|
Show details
|
|
47 |
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
|
|
|
|
BASE
|
|
Show details
|
|
48 |
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
49 |
Cross-lingual semantic specialization via lexical relation induction ...
|
|
|
|
BASE
|
|
Show details
|
|
50 |
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ...
|
|
|
|
BASE
|
|
Show details
|
|
51 |
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
52 |
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
|
|
|
|
BASE
|
|
Show details
|
|
53 |
Do we really need fully unsupervised cross-lingual embeddings? ...
|
|
|
|
Abstract:
Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-lingual space without any cross-lingual signal. The lack of any supervision makes such approaches conceptually attractive. Yet, their only core difference from (weakly) supervised projection-based CLWE methods is in the way they obtain a seed dictionary used to initialize an iterative self-learning procedure. The fully unsupervised methods have arguably become more robust, and their primary use case is CLWE induction for pairs of resource-poor and distant languages. In this paper, we question the ability of even the most robust unsupervised CLWE approaches to induce meaningful CLWEs in these more challenging settings. A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e.g., they yield zero ...
|
|
URL: https://www.repository.cam.ac.uk/handle/1810/296966 https://dx.doi.org/10.17863/cam.44007
|
|
BASE
|
|
Hide details
|
|
54 |
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
55 |
Probing Pretrained Language Models for Lexical Semantics ...
|
|
|
|
BASE
|
|
Show details
|
|
56 |
Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction ...
|
|
|
|
BASE
|
|
Show details
|
|
57 |
On the relation between linguistic typology and (limitations of) multilingual language modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
58 |
Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ...
|
|
|
|
BASE
|
|
Show details
|
|
59 |
The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ...
|
|
|
|
BASE
|
|
Show details
|
|
60 |
Spatial multi-arrangement for clustering and multi-way similarity dataset construction ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|