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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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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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Socially Aware Bias Measurements for Hindi Language Representations ...
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Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution ...
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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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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 ...
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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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GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction ...
Abstract: Recent progress in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations such that models trained on one language can be applied to other languages. However, GCNs struggle to model words with long-range dependencies or are not directly connected in the dependency tree. To address these challenges, we propose to utilize the self-attention mechanism where we explicitly fuse structural information to learn the dependencies between words with different syntactic distances. We introduce GATE, a {\bf G}raph {\bf A}ttention {\bf T}ransformer {\bf E}ncoder, and test its cross-lingual transferability on relation and event extraction tasks. We perform experiments on the ACE05 dataset that includes three typologically different languages: English, Chinese, and Arabic. The evaluation results show that GATE outperforms three recently proposed methods by a large margin. Our detailed analysis reveals that due ... : AAAI 2021 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2010.03009
https://arxiv.org/abs/2010.03009
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