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Word vector representations are a crucial part of Natural Language Processing (NLP) and Human Computer Interaction.
Compositional morphology for word representations and language modelling
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Janus-iii: Speech-to-speech translation in multiple languages
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Placing search in context: The concept revisited
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Improving spoken language understanding using word confusion networks
Tur, G., Wright, J., Gorin, A., Riccardi, G., and Hakkani-Tür, D. (2002) · 2002
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A neural probabilistic language model
Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C. (2003) · 2003
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Latent dirichlet allocation
Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003) · 2003
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The fisher corpus: a resource for the next generations of speech-to-text
Cieri, C., Miller, D., and Walker, K. (2004) · 2004
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Monolingual machine translation for paraphrase generation
Quirk, C., Brockett, C., and Dolan, W. (2004) · 2004
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Using word latice information for a tighter coupling in speech translation systems
Schultz, T., Jou, S.-C., Vogel, S., and Saleem, S. (2004) · 2004
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Confidence measures for speech recognition: A survey
Jiang, H. (2005) · 2005
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On the integration of speech recognition and statistical machine translation
Matusov, E., Kanthak, S., and Ney, H. (2005) · 2005
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Speech repair: Quick error correction just by using selection operation for speech input interfaces
Ogata, J. and Goto, M. (2005) · 2005
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Improved confusion network algorithm and shortest path search from word lattice
Xue, J. and Zhao, Y. (2005) · 2005
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Beyond asr 1-best: Using word confusion networks in spoken language understanding
Hakkani-Tür, D., Béchet, F., Riccardi, G., and Tur, G. (2006) · 2006
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Statistical phrase-based speech translation
Mathias, L. and Byrne, W. (2006) · 2006
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Computing consensus translation for multiple machine translation systems using enhanced hypothesis alignment
Matusov, E., Ueffing, N., and Ney, H. (2006) · 2006
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Error detection in confusion network
Allauzen, A. (2007) · 2007
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Speech translation by confusion network decoding
Bertoldi, N., Zens, R., and Federico, M. (2007) · 2007
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Analysis of statistical and morphological classes to generate weighted reordering hypotheses on a statistical machine translation system
Costa-jussà, M. R. and Fonollosa, J. A. (2007) · 2007
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The’noisier channel’: translation from morphologically complex languages
Dyer, C. J. (2007) · 2007
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Cross-site and intra-site asr system combination: Comparisons on lattice and 1-best methods
Hoffmeister, B., Hillard, D., Hahn, S., Schluter, R., Ostendor, M., and Ney, H. (2007) · 2007
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Open-vocabulary spoken utterance retrieval using confusion networks
Hori, T., Hetherington, I. L., Hazen, T. J., and Glass, J. R. (2007) · 2007
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Combining outputs from multiple machine translation systems
Rosti, A.-V., Ayan, N. F., Xiang, B., Matsoukas, S., Schwartz, R., and Dorr, B. (2007a) · 2007
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Retrieval and browsing of spoken content
Chelba, C., Hazen, T. J., and Saraclar, M. (2008) · 2008
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Generalizing word lattice translation
Dyer, C., Muresan, S., and Resnik, P. (2008) · 2008
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Using a maximum entropy model to build segmentation lattices for mt
Dyer, C. (2009) · 2009
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An error-driven word-character hybrid model for joint chinese word segmentation and pos tagging
Kruengkrai, C., Uchimoto, K., Kazama, J., Wang, Y., Torisawa, K., and Isahara, H. (2009) · 2009
Cited alongside, same era.
A pos-based model for long-range reorderings in smt
Niehues, J. and Kolss, M. (2009) · 2009
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Word lattices for multi-source translation
Schroeder, J., Cohn, T., and Koehn, P. (2009) · 2009
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A formal model of ambiguity and its applications in machine translation
Dyer, C. J. (2010) · 2010
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Why does unsupervised pre-training help deep learning?
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.-A., Vincent, P., and Bengio, S. (2010) · 2010
Efficient lattice rescoring using recurrent neural network language models
Liu, X., Wang, Y., Chen, X., Gales, M. J., and Woodland, P. C. (2014) · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. (2014) · 2014
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Co-learning of word representations and morpheme representations
Qiu, S., Cui, Q., Bian, J., Gao, B., and Liu, T.-Y. (2014) · 2014
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Lattice decoding and rescoring with long-span neural network language models
Sundermeyer, M., Tüske, Z., Schlüter, R., and Ney, H. (2014) · 2014
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Document classification with distributions of word vectors
Xing, C., Wang, D., Zhang, X., and Liu, C. (2014) · 2014
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Joint learning of character and word embeddings
Chen, X., Xu, L., Liu, Z., Sun, M., and Luan, H.-B. (2015) · 2015
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Word lattices for morphological reduction and chunk-based reordering
Hardmeier, C., Bisazza, A., and Federico, M. (2010) · 2010
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Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černockỳ, J., and Khudanpur, S. (2010) · 2010
Cited alongside, same era.
Automatic speech recognition system channel modeling
Tan, Q. F., Audhkhasi, K., Georgiou, P. G., Ettelaie, E., and Narayanan, S. S. (2010) · 2010
Cited alongside, same era.
Training of error-corrective model for ASR without using audio data
Kurata, G., Itoh, N., and Nishimura, M. (2011) · 2011
Cited alongside, same era.
Paraphrase lattice for statistical machine translation
Onishi, T., Utiyama, M., and Sumita, E. (2011) · 2011
Cited alongside, same era.
The kaldi speech recognition toolkit
Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., Silovsky, J., Stemmer, G., and Vesely, K. (2011) · 2011
Cited alongside, same era.
Later among the works it cites.
Morphological word-embeddings
Cotterell, R. and Schütze, H. (2015) · 2015
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Combining continuous word representation and prosodic features for asr error prediction
Ghannay, S., Estève, Y., Camelin, N., Dutrey, C., Santiago, F., and Adda-Decker, M. (2015a) · 2015
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Word embeddings combination and neural networks for robustness in asr error detection
Ghannay, S., Estève, Y., and Camelin, N. (2015b) · 2015
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Support vector machines and word2vec for text classification with semantic features
Lilleberg, J., Zhu, Y., and Zhang, Y. (2015) · 2015
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Two/too simple adaptations of word2vec for syntax problems
Ling, W., Dyer, C., Black, A. W., and Trancoso, I. (2015) · 2015
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Evaluation methods for unsupervised word embeddings
Schnabel, T., Labutov, I., Mimno, D., and Joachims, T. (2015) · 2015
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Unsupervised morphology induction using word embeddings
Soricut, R. and Och, F. (2015) · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Tai, K. S., Socher, R., and Manning, C. D. (2015) · 2015
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Tensorflow: a system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · 2016
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Chung, Y.-A., Wu, C.-C., Shen, C.-H., Lee, H.-Y., and Lee, L.-S. (2016) · 2016
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Acoustic word embeddings for asr error detection
Ghannay, S., Estève, Y., Camelin, N., and deléglise, P. (2016) · 2016
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Multi-view recurrent neural acoustic word embeddings
He, W., Wang, W., and Livescu, K. (2016) · 2016
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Bag of tricks for efficient text classification
Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2016) · 2016
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Deep convolutional acoustic word embeddings using word-pair side information
Kamper, H., Wang, W., and Livescu, K. (2016) · 2016
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Latticernn: Recurrent neural networks over lattices
Ladhak, F., Gandhe, A., Dreyer, M., Mathias, L., Rastrow, A., and Hoffmeister, B. (2016) · 2016
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Achieving human parity in conversational speech recognition
Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., and Zweig, G. (2016) · 2016
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Yin, W. and Schütze, H. (2016) · 2016
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Enriching word vectors with subword information
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017) · 2017
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Neural lattice-to-sequence models for uncertain inputs
Sperber, M., Neubig, G., Niehues, J., and Waibel, A. (2017) · 2017
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Lattice-based recurrent neural network encoders for neural machine translation
Su, J., Tan, Z., Xiong, D., Ji, R., Shi, X., and Liu, Y. (2017) · 2017
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Neural lattice language models
Buckman, J. and Neubig, G. (2018) · 2018
Closest in time.
Shivakumar, P. G., Li, H., Knight, K., and Georgiou, P. (2018) · 2018
Closest in time.
Lattice-to-sequence attentional neural machine translation models
Tan, Z., Su, J., Wang, B., Chen, Y., and Shi, X. (2018) · 2018
Closest in time.