Part-of-Speech Tagging with Recurrent Neural Networks

Part-of-Speech Tagging with Recurrent Neural Networks

Lookahead Convolution Layer for Unidirec- Tional Recurrent Neural Networks

Recurrent neural networks (RNNs) have been shown to be very effective for many sequential prediction problems such as speech recognition, machine translation, part-of-speech tagging, and others. The best variant is typically a bidirectional RNN that learns representation for a sequence by performing a forward and a backward pass through the entire sequence. However, unlike unidirectional RNNs, ...

Implicitly-Defined Neural Networks for Sequence Labeling

In this work, we propose a novel, implicitlydefined neural network architecture and describe a method to compute its components. The proposed architecture forgoes the causality assumption previously used to formulate recurrent neural networks and allow the hidden states of the network to coupled together, allowing potential improvement on problems with complex, long-distance dependencies. Initi...

Global Syllable Vectors for Building TTS Front-End with Deep Learning

Recent vector space representations of words have succeeded in capturing syntactic and semantic regularities. In the context of text-to-speech (TTS) synthesis, a front-end is a key component for extracting multi-level linguistic features from text, where syllable acts as a link between lowand high-level features. This paper describes the use of global syllable vectors as features to build a fro...

Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks

In this paper, we propose a novel approach to induce automatically a Part-Of-Speech (POS) tagger for resource-poor languages (languages that have no labeled training data). This approach is based on cross-language projection of linguistic annotations from parallel corpora without the use of word alignment information. Our approach does not assume any knowledge about foreign languages, making it...