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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
Automatic Error Type Annotation for Arabic ...
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3
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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Detecting Gender Bias using Explainability ...
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HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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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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9
Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
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10
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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11
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
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Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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13
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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14
Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
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CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
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Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.817/ Abstract: A new metric BaryScore to evaluate text generation based on deep contextualized embeddings (e.g, BERT, Roberta, ELMo) is introduced. This metric is motivated by a new framework relying on optimal transport tools, i.e, Wasserstein distance and barycenter. By modelling the layer output of deep contextualized embeddings as a probability distribution rather than by a vector embedding; this framework provides a natural way to aggregate the different outputs through the Wasserstein space topology. In addition, it provides theoretical grounds to our metric and offers an alternative to available solutions (e.g, MoverScore and BertScore). Numerical evaluation is performed on four different tasks: machine translation, summarization, data2text generation and image captioning. Our results show that BaryScore outperforms other BERT based metrics and exhibits more consistent behaviour in particular for text summarization. ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Generation
URL: https://dx.doi.org/10.48448/yrzd-ge67
https://underline.io/lecture/37437-automatic-text-evaluation-through-the-lens-of-wasserstein-barycenters
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18
Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
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19
Meta Distant Transfer Learning for Pre-trained Language Models ...
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20
How to Train BERT with an Academic Budget ...
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