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Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin
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.
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
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.
On the Cross-lingual Transferability of Monolingual Representations
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).
Grandmaster level in StarCraft II using multi-agent reinforcement learning
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.
Generative and Discriminative Text Classification with Recurrent Neural Networks
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.
Learning to Compose Words into Sentences with Reinforcement Learning
- Dani Yogatama, P. Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
- Computer ScienceICLR
- 4 November 2016
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.
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Sparse Overcomplete Word Vector Representations
- Manaal Faruqui, Yulia Tsvetkov, Dani Yogatama, Chris Dyer, Noah A. Smith
- Computer ScienceACL
- 5 June 2015
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.
Episodic Memory in Lifelong Language Learning
- Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama
- Computer ScienceNeurIPS
- 1 June 2019
This work proposes an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier.
Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.