Ashish Vaswani

Supertagging With LSTMs

2016

Ashish Vaswani, Yonatan Bisk, Kenji Sagae, Ryan Musa. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

Attention Is All You Need

2025

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a …

Fast Decoding in Sequence Models using Discrete Latent Variables

2018 · arXiv (Cornell University)

Autoregressive sequence models based on deep neural networks, such as RNNs, Wavenet and the Transformer attain state-of-the-art results on many tasks. However, they are difficult to parallelize and are thus slow at processing long sequences. …

Unsupervised Neural Hidden Markov Models

2016

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag induction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though …