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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 ...
Abstract: State-of-the-art contextualized models such as BERT use tasks such as WiC and WSD to evaluate their word-in-context representations. This inherently assumes that performance in these tasks reflect how well a model represents the coupled word and context semantics. This study investigates this assumption by presenting the first quantitative analysis (using probing baselines) on the context-word interaction being tested in major contextual lexical semantic tasks. Specifically, based on the probing baseline performance, we propose measures to calculate the degree of context or word biases in a dataset, and plot existing datasets on a continuum. The analysis shows most existing datasets fall into the extreme ends of the continuum (i.e. they are either heavily context-biased or target-word-biased) while only AM$^2$iCo and Sense Retrieval challenge a model to represent both the context and target words. Our case study on WiC reveals that human subjects do not share models' strong context biases in the dataset ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2112.06733
https://arxiv.org/abs/2112.06733
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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 ...
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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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