An introduction to deep learning in natural language processing: Models, techniques, and tools
Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …
involves the design and implementation of systems and algorithms able to interact through …
Spanish pre-trained bert model and evaluation data
The Spanish language is one of the top 5 spoken languages in the world. Nevertheless,
finding resources to train or evaluate Spanish language models is not an easy task. In this …
finding resources to train or evaluate Spanish language models is not an easy task. In this …
Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries
It is often difficult for a machine learning model trained based on a small size of
charge/discharge cycling data to produce satisfactory accuracy in the capacity estimation of …
charge/discharge cycling data to produce satisfactory accuracy in the capacity estimation of …
Deep transfer learning for automatic speech recognition: Towards better generalization
Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …
using deep learning (DL). It requires large-scale training datasets and high computational …
Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism
Named entity recognition (NER) is an important task in natural language processing area,
which needs to determine entities boundaries and classify them into pre-defined categories …
which needs to determine entities boundaries and classify them into pre-defined categories …
Choosing transfer languages for cross-lingual learning
Cross-lingual transfer, where a high-resource transfer language is used to improve the
accuracy of a low-resource task language, is now an invaluable tool for improving …
accuracy of a low-resource task language, is now an invaluable tool for improving …
A survey on recent advances in sequence labeling from deep learning models
Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks,
eg, part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc …
eg, part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc …
Robust multilingual part-of-speech tagging via adversarial training
Adversarial training (AT) is a powerful regularization method for neural networks, aiming to
achieve robustness to input perturbations. Yet, the specific effects of the robustness obtained …
achieve robustness to input perturbations. Yet, the specific effects of the robustness obtained …
Dual adversarial neural transfer for low-resource named entity recognition
We propose a new neural transfer method termed Dual Adversarial Transfer Network
(DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two …
(DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two …
On difficulties of cross-lingual transfer with order differences: A case study on dependency parsing
Different languages might have different word orders. In this paper, we investigate cross-
lingual transfer and posit that an order-agnostic model will perform better when transferring …
lingual transfer and posit that an order-agnostic model will perform better when transferring …