What I read in August

31 Aug 2022 reading by Vagrant Gautam

If you think I read too much, you haven't met my friends.

For fun and edification

Books

McPherron, P., & Ramanathan, V. (2011). Language, body, and health.
Yergeau, M. (2017). Authoring autism: on rhetoric and neurological queerness.
McRuer, R. (2006). Crip theory: cultural signs of queerness and disability.
Feinberg, L. (1993). Stone butch blues.

Papers

Irvine, J. T., & Gal, S. (2000). Language Ideology and Linguistic Differentiation.
Cuboniks, L. (2015). Xenofeminism: A Politics for Alienation.
Toze, M. (2019). Developing a critical trans gerontology.
Bucholtz, M., & Hall, K. (2016). Embodied sociolinguistics.
Ahearn, L. M. (2012). Language, Thought, and Culture.
Schoenebeck, S., & Blackwell, L. (2021). Reimagining Social Media Governance: Harm, Accountability, and Repair.
Heilker, P., & Yergeau, M. (2011). Autism and Rhetoric.
Konnelly, L. (2021). Nuance and normativity in trans linguistic research.
Andrus, J. (2012). Language ideology, fractal recursivity, and discursive agency in the legal construction of linguistic evidence.
Bauer, H. (2010). 'Race', normativity and the history of sexuality: Magnus Hirschfeld's racism and the early-twentieth-century sexology.

Other

150 pages of readings for the University of Alberta's Palaeontology: Theropod Dinosaurs and the Origin of Birds course

For pay

Papers

Singh, M., Oualil, Y., & Klakow, D. (2017). Approximated and domain-adapted LSTM language models for first-pass decoding in speech recognition.
Deena, S., Hasan, M., Doulaty, M., Saz, O., & Hain, T. (2016). Combining feature and model-based adaptation of RNNLMs for multi-genre broadcast speech recognition.
Huang, Z., Zweig, G., & Dumoulin, B. (2014). Cache based recurrent neural network language model inference for first pass speech recognition.
Klakow, D. (2006). Language model adaptation for tiny adaptation corpora.
Kneser, R., Peters, J., & Klakow, D. (1997). Language model adaptation using dynamic marginals.
Besling, S., & Meier, H.-G. (1995). Language Model Speaker Adaptation.
Kneser, R., & Ney, H. (1993). Improved clustering techniques for class-based statistical language modelling.
Moore, G., & Young, S. (2000). Class-based language model adaptation using mixtures of word-class weights.
Shi, Y., Wiggers, P., & Jonker, C. M. (2012). Towards recurrent neural networks language models with linguistic and contextual features.
Bengio, Y., Ducharme, R., & Vincent, P. (2003). A Neural Probabilistic Language Model.
Klakow, D. (2000). Selecting articles from the language model training corpus.
Adel, H., Kirchhoff, K., Vu, N. T., Telaar, D., & Schultz, T. (2014). Comparing approaches to convert recurrent neural networks into backoff language models for efficient decoding.
Jalalvand, S. (2013). Improving Language Model Adaptation using Automatic Data Selection and Neural Network.
Bacchiani, M., Roark, B., & Saraclar, M. (2004). Language model adaptation with MAP estimation and the perceptron algorithm.
Sanchis-Trilles, G., & Cettolo, M. (2010). Online language model adaptation via n-gram mixtures for statistical machine translation.
Vapnik, V. (1991). Principles of Risk Minimization for Learning Theory.
Mikolov, T., Karafiat, M., Burget, L., Cernocky, J., & Khudanpur, S. (2010). Recurrent Neural Network Based Language Model.
Chen, X., Liu, X., Gales, M. J. F., & Woodland, P. C. (2015). Improving the training and evaluation efficiency of recurrent neural network language models.
Chen, X., Liu, X., Wang, Y., Gales, M. J. F., & Woodland, P. C. (2016). Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition.
Mikolov, T., Yih, W., & Zweig, G. (2013). Linguistic Regularities in Continuous Space Word Representations.
Chen, X., Tan, T., Liu, X., Lanchantin, P., Wan, M., Gales, M. J. F., & Woodland, P. C. (2015). Recurrent neural network language model adaptation for multi-genre broadcast speech recognition.
Gangireddy, S. R., Swietojanski, P., Bell, P., & Renals, S. (2016). Unsupervised Adaptation of Recurrent Neural Network Language Models.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting.
Li, K., Xu, H., Wang, Y., Povey, D., & Khudanpur, S. (2018). Recurrent Neural Network Language Model Adaptation for Conversational Speech Recognition.
Kuhn, R., & De Mori, R. (1990). A cache-based natural language model for speech recognition.
Grave, E., Joulin, A., & Usunier, N. (2017). Improving neural language models with a continuous cache.
Grave, E., Cisse, M. M., & Joulin, A. (2017). Unbounded cache model for online language modeling with open vocabulary.
Lau, R., Rosenfeld, R., & Roukos, S. (1993). Trigger-based language models: a maximum entropy approach.
Singh-Miller, N., & Collins, M. (2007). Trigger-Based Language Modeling using a Loss-Sensitive Perceptron Algorithm.
Deoras, A., Mikolov, T., Kombrink, S., Karafiat, M., & Khudanpur, S. (2011). Variational approximation of long-span language models for LVCSR.
Chu, C., & Wang, R. (2018). A Survey of Domain Adaptation for Neural Machine Translation.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors.
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information.
Singh, M., Virpioja, S., Smit, P., & Kurimo, M. (2019). Subword RNNLM Approximations for Out-Of-Vocabulary Keyword Search.
Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global Vectors for Word Representation.

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