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Enhanced LSTM for Natural Language Inference
TLDR
This paper presents a new state-of-the-art result, achieving the accuracy of 88.6% on the Stanford Natural Language Inference Dataset, and demonstrates that carefully designing sequential inference models based on chain LSTMs can outperform all previous models.
Convolutional Neural Networks for Speech Recognition
TLDR
It is shown that further error rate reduction can be obtained by using convolutional neural networks (CNNs), and a limited-weight-sharing scheme is proposed that can better model speech features.
Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition
TLDR
The proposed CNN architecture is applied to speech recognition within the framework of hybrid NN-HMM model to use local filtering and max-pooling in frequency domain to normalize speaker variance to achieve higher multi-speaker speech recognition performance.
Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference
TLDR
This paper presents a new state-of-the-art result, achieving the accuracy of 88.3% on the standard benchmark, the Stanford Natural Language Inference dataset, through an enhanced sequential encoding model, which outperforms the previous best model that employs more complicated network architectures.
Large margin hidden Markov models for speech recognition
TLDR
A novel method to estimate continuous-density hidden Markov model (CDHMM) for speech recognition according to the principle of maximizing the minimum multiclass separation margin by using a penalized gradient descent algorithm.
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
TLDR
This paper describes a model (alpha) that is ranked among the top in the Shared Task, on both the in- domain test set and on the cross-domain test set, demonstrating that the model generalizes well to theCross-domain data.
Generating images with recurrent adversarial networks
TLDR
This work proposes a recurrent generative model that can be trained using adversarial training to generate very good image samples, and proposes a way to quantitatively compare adversarial networks by having the generators and discriminators of these networks compete against each other.
A Local Detection Approach for Named Entity Recognition and Mention Detection
TLDR
A new local detection approach for named entity recognition (NER) and mention detection (MD) in natural language processing is proposed, which relies on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixed- size representation.
The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models
TLDR
Experimental results have shown that without using any recurrent feedbacks, FOFE based FNNLMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular recurrent neural network (RNN) LMs.
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