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Observation of new excited ${B} ^0_{s} $ states
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In: Eur.Phys.J.C ; https://hal.archives-ouvertes.fr/hal-03010999 ; Eur.Phys.J.C, 2021, 81 (7), pp.601. ⟨10.1140/epjc/s10052-021-09305-3⟩ (2021)
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End-to-end Speech Translation via Cross-modal Progressive Training ...
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Locate then Segment: A Strong Pipeline for Referring Image Segmentation ...
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Learning Language Specific Sub-network for Multilingual Machine Translation ...
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Contrastive Learning for Many-to-many Multilingual Neural Machine Translation ...
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Multilingual Translation via Grafting Pre-trained Language Models ...
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Counter-Interference Adapter for Multilingual Machine Translation ...
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MTG: A Benchmarking Suite for Multilingual Text Generation ...
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Language Tags Matter for Zero-Shot Neural Machine Translation ...
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Personalized Transformer for Explainable Recommendation ...
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Dynamic Knowledge Distillation for Pre-trained Language Models ...
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Learning Shared Semantic Space for Speech-to-Text Translation ...
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Glancing Transformer for Non-Autoregressive Neural Machine Translation ...
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Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.711/ Abstract: Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations. Traditional methods first devise task-specific operations such as Synonym Substitute, then preset the corresponding parameters such as the substitution rate artificially, which require a lot of prior knowledge and are prone to fall into the sub-optimum. Besides, the number of editing operations is limited in the previous methods, which decreases the diversity of the augmented data and thus restricts the performance gain. To overcome the above limitations, we propose a framework named Text AutoAugment (TAA) to establish a compositional and learnable paradigm for data augmentation. We regard a combination of various operations as an augmentation policy and utilize an efficient Bayesian Optimization algorithm to automatically search for the best policy, which substantially improves the generalization ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/a210-qg54 https://underline.io/lecture/37434-text-autoaugment-learning-compositional-augmentation-policy-for-text-classification
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Probabilistic Graph Reasoning for Natural Proof Generation ...
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Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker ...
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Multilingual Translation via Grafting Pre-trained Language Models ...
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Language Tags Matter for Zero-Shot Neural Machine Translation ...
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