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1
Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction ...
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Cross-lingual Representation Learning for Natural Language Processing
Ahmad, Wasi Uddin. - : eScholarship, University of California, 2021
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Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ...
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4
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training ...
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5
Societal Biases in Language Generation: Progress and Challenges ...
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6
Socially Aware Bias Measurements for Hindi Language Representations ...
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7
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution ...
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8
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification ...
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9
Intent Classification and Slot Filling for Privacy Policies ...
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10
Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning ...
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11
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ...
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12
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble ...
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13
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training ...
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14
Syntax-augmented Multilingual BERT for Cross-lingual Transfer ...
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15
Syntax-augmented Multilingual BERT for Cross-lingual Transfer ...
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16
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation ...
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17
"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition ...
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18
Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer ...
Abstract: Multilingual representations embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language. These embeddings have been widely used in various settings, such as cross-lingual transfer, where a natural language processing (NLP) model trained on one language is deployed to another language. While the cross-lingual transfer techniques are powerful, they carry gender bias from the source to target languages. In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations from both the intrinsic and extrinsic perspectives. Experimental results show that the magnitude of bias in the multilingual representations changes differently when we align the embeddings to different target spaces and that the alignment direction can also have an influence ...
Keyword: Computation and Language cs.CL; Computers and Society cs.CY; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.2005.00699
https://arxiv.org/abs/2005.00699
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19
On the Robustness of Language Encoders against Grammatical Errors ...
Yin, Fan; Long, Quanyu; Meng, Tao. - : arXiv, 2020
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20
GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction ...
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