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Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin
TLDR
It is shown that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech-two vastly different languages, and is competitive with the transcription of human workers when benchmarked on standard datasets. Expand
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
TLDR
A tagset is developed, data is annotated, features are developed, and results nearing 90% accuracy are reported on the problem of part-of-speech tagging for English data from the popular micro-blogging service Twitter. Expand
Grandmaster level in StarCraft II using multi-agent reinforcement learning
TLDR
The agent, AlphaStar, is evaluated, which uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II. Expand
On the Cross-lingual Transferability of Monolingual Representations
TLDR
This work designs an alternative approach that transfers a monolingual model to new languages at the lexical level and shows that it is competitive with multilingual BERT on standard cross-lingUAL classification benchmarks and on a new Cross-lingual Question Answering Dataset (XQuAD). Expand
Generative and Discriminative Text Classification with Recurrent Neural Networks
TLDR
Although RNN-based generative models are more powerful than their bag-of-words ancestors, they have higher asymptotic error rates than discriminatively trained RNN models, and it is hypothesized that RNN based generative classification models will be more robust to shifts in the data distribution. Expand
Learning to Compose Words into Sentences with Reinforcement Learning
TLDR
Reinforcement learning is used to learn tree-structured neural networks for computing representations of natural language sentences and it is shown that while they discover some linguistically intuitive structures, they are different than conventional English syntactic structures. Expand
Sparse Overcomplete Word Vector Representations
TLDR
This work proposes methods that transform word vectors into sparse (and optionally binary) vectors, which are more similar to the interpretable features typically used in NLP, though they are discovered automatically from raw corpora. Expand
Efficient Transfer Learning Method for Automatic Hyperparameter Tuning
TLDR
This work proposes a fast and effective algorithm for automatic hyperparameter tuning that can generalize across datasets and empirically shows the superiority of the method on a large number of synthetic and real-world datasets for tuning hyperparameters of logistic regression and ensembles of classifiers. Expand
Embedding Methods for Fine Grained Entity Type Classification
TLDR
A new approach based on label embeddings that allows for information sharing among related labels that outperforms state-of-the-art methods on two fine grained entity-classification benchmarks and can exploit the finer-grained labels to improve classification of standard coarse types. Expand
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