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1
Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
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2
Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
Wei, Megan. - : Open Science Framework, 2022
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3
Easy-to-use combination of POS and BERT model for domain-specific and misspelled terms
In: NL4IA Workshop Proceedings ; https://hal.archives-ouvertes.fr/hal-03474696 ; NL4IA Workshop Proceedings, Nov 2021, Milan, Italy (2021)
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Globalizing BERT-based Transformer Architectures for Long DocumentSummarization
In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume ; 16th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.univ-grenoble-alpes.fr/hal-03367913 ; 16th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Apr 2021, Online, France (2021)
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5
Automatic Error Type Annotation for Arabic ...
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Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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8
Detecting Gender Bias using Explainability ...
Abstract: Explanations for AI systems have been used to improve the trustworthiness of these systems. These explanations can be used to find the undesirable implicit biases that machine learning models can rely on for their outputs. We apply this concept to detect gender bias in sentiment analysis models for textual data. With the help of an Equity Evaluation Corpus (EEC), we used different gender signals for otherwise identical input to the system and use explanations from LIME and SHAP to find a trend of bias, and identify terms that contribute the most to it. ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis
URL: https://dx.doi.org/10.48448/xtya-7b69
https://underline.io/lecture/39696-detecting-gender-bias-using-explainability
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9
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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10
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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Contrastive Code Representation Learning ...
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12
Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
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13
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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14
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
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15
Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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16
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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17
Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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18
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
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19
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
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20
Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
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