Open-Domain Name Error Detection using a Multi-Task RNN

Out-of-vocabulary name errors in speech recognition create significant problems for downstream language processing, but the fact that they are rare poses challenges for automatic detection, particularly in an open-domain scenario. To address this problem, a multi-task recurrent neural network language model for sentence-level name detection is proposed for use in combination with out-of-vocabulary word detection. The sentence-level model is also effective for leveraging external text data. Experiments show a 26% improvement in name-error detection F-score over a system using n-gram lexical features.

[1]  Mari Ostendorf,et al.  Improving out-of-vocabulary name resolution , 2005, Comput. Speech Lang..

[2]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[3]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[4]  Mark Dredze,et al.  OOV Sensitive Named-Entity Recognition in Speech , 2011, INTERSPEECH.

[5]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[6]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[7]  Andrew Y. Ng,et al.  Semantic Compositionality through Recursive Matrix-Vector Spaces , 2012, EMNLP.

[8]  Jasha Droppo,et al.  Multi-task learning in deep neural networks for improved phoneme recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Christopher D. Manning,et al.  Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[10]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[11]  Xiaodong Liu,et al.  Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval , 2015, NAACL.

[12]  Simon King,et al.  Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[14]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[15]  Wei Chen,et al.  Variable-Span out-of-vocabulary named entity detection , 2013, INTERSPEECH.

[16]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[17]  Jun Zhao,et al.  Recurrent Convolutional Neural Networks for Text Classification , 2015, AAAI.

[18]  Mari Ostendorf,et al.  Effective data-driven feature learning for detecting name errors in automatic speech recognition , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).

[19]  Mari Ostendorf,et al.  Learning phrase patterns for ASR name error detection using semantic similarity , 2015, INTERSPEECH.

[20]  Geoffrey Zweig,et al.  Linguistic Regularities in Continuous Space Word Representations , 2013, NAACL.

[21]  Katsuhito Sudoh,et al.  Incorporating Speech Recognition Confidence into Discriminative Named Entity Recognition of Speech Data , 2006, ACL.

[22]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[23]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[24]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[25]  Rich Caruana,et al.  Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.

[26]  Marius Marin,et al.  Effective Use of Cross-Domain Parsing in Automatic Speech Recognition and Error Detection , 2015 .

[27]  Frédéric Béchet,et al.  “Can you give me another word for hyperbaric?”: Improving speech translation using targeted clarification questions , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[28]  Yee Whye Teh,et al.  A fast and simple algorithm for training neural probabilistic language models , 2012, ICML.