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1
Delving Deeper into Cross-lingual Visual Question Answering ...
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2
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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3
Improving Word Translation via Two-Stage Contrastive Learning ...
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4
Towards Zero-shot Language Modeling ...
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5
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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6
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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7
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification ...
Zhu, Yi; Shareghi, Ehsan; Li, Yingzhen. - : arXiv, 2021
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8
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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9
Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets ...
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10
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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11
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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12
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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13
Emergent Communication Pretraining for Few-Shot Machine Translation ...
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14
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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15
Verb Knowledge Injection for Multilingual Event Processing ...
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16
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
Abstract: We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering datasets for 12 typologically diverse languages, including major languages (e.g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e.g., Welsh, Kiswahili). Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs, providing a representative coverage of word classes (nouns, verbs, adjectives, adverbs), frequency ranks, similarity intervals, lexical fields, and concreteness levels. Additionally, owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets. Due to its extensive size and language coverage, Multi-SimLex provides entirely novel opportunities for experimental evaluation and analysis. On its monolingual and cross-lingual benchmarks, we evaluate and analyze a wide array of recent state-of-the-art monolingual and cross-lingual representation models, ... : Data and guidelines available at https://multisimlex.com/ ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2003.04866
https://arxiv.org/abs/2003.04866
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17
Probing Pretrained Language Models for Lexical Semantics ...
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18
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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19
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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20
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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