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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
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42 |
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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43 |
Verb Knowledge Injection for Multilingual Event Processing ...
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44 |
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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45 |
Probing Pretrained Language Models for Lexical Semantics ...
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The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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47 |
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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48 |
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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49 |
Cross-lingual semantic specialization via lexical relation induction ...
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ...
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51 |
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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52 |
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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Do we really need fully unsupervised cross-lingual embeddings? ...
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54 |
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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55 |
Probing Pretrained Language Models for Lexical Semantics ...
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56 |
Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction ...
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Abstract:
Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure. It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings. In this work, we present ClassyMap, a classification-based approach to self-learning, yielding a more robust and a more effective induction of projection-based CLWEs. Unlike prior self-learning methods, our approach allows for integration of diverse features into the iterative process. We show the benefits of ClassyMap for bilingual lexicon induction: we report consistent improvements in a weakly supervised setup (500 seed translation pairs) on a benchmark with 28 language pairs. ...
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URL: https://dx.doi.org/10.17863/cam.53930 https://www.repository.cam.ac.uk/handle/1810/306839
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57 |
On the relation between linguistic typology and (limitations of) multilingual language modeling ...
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58 |
Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ...
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59 |
The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ...
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60 |
Spatial multi-arrangement for clustering and multi-way similarity dataset construction ...
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