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Enhanced LSTM for Natural Language Inference
Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (Bowman et al.,Expand
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Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference
Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is notoriously challenging but is fundamental to natural language understanding and manyExpand
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Large margin hidden Markov models for speech recognition
In this paper, motivated by large margin classifiers in machine learning, we propose a novel method to estimate continuous-density hidden Markov model (CDHMM) for speech recognition according to theExpand
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Confidence measures for speech recognition: A survey
  • Hui Jiang
  • Computer Science
  • Speech Commun.
  • 1 April 2005
Abstract In speech recognition, confidence measures (CM) are used to evaluate reliability of recognition results. A good confidence measure can largely benefit speech recognition systems in manyExpand
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Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks andExpand
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The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models
In this paper, we propose the new fixedsize ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFEExpand
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Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints
In this paper, we propose a general framework to incorporate semantic knowledge into the popular data-driven learning process of word embeddings to improve the quality of them. Under this framework,Expand
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A Local Detection Approach for Named Entity Recognition and Mention Detection
In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, weExpand
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Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency
In this paper, we propose a novel neural network structure, namely \emph{feedforward sequential memory networks (FSMN)}, to model long-term dependency in time series without using recurrent feedback.Expand
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Distraction-based neural networks for modeling documents
Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether andExpand
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