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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
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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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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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Intent Classification and Slot Filling for Privacy Policies ...
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Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning ...
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Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ...
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Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble ...
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Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training ...
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Syntax-augmented Multilingual BERT for Cross-lingual Transfer ...
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Syntax-augmented Multilingual BERT for Cross-lingual Transfer ...
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BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation ...
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"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition ...
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Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer ...
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On the Robustness of Language Encoders against Grammatical Errors ...
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Abstract:
We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors. Specifically, we collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. We use this approach to facilitate debugging models on downstream applications. Results confirm that the performance of all tested models is affected but the degree of impact varies. To interpret model behaviors, we further design a linguistic acceptability task to reveal their abilities in identifying ungrammatical sentences and the position of errors. We find that fixed contextual encoders with a simple classifier trained on the prediction of sentence correctness are able to locate error positions. We also design a cloze test for BERT and discover that BERT captures the interaction between errors and specific tokens in context. Our results shed light on understanding the robustness and behaviors of ... : ACL 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2005.05683 https://arxiv.org/abs/2005.05683
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GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction ...
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