Neural network based language models for highly inflective languages

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OPT: Open Pre-trained Transformer Language Models (2022, background): “Auto-regressive language models (Mikolov et al., 2009) have seen the largest growth in model size, from 117M parameters (Radford et al.”
Learning to Reweight Terms with Distributed Representations (2015, background): “In this section, we briefly introduce recent work on distributed representations learning from neural network language models and also defines the target term weights we use in our framework.”
Empirical Evaluation and Combination of Advanced Language Modeling Techniques (2011, background): “Emami’s model was a state of the art model on this dataset, until we have recently shown that Recurrent neural network based language model (RNNLM) is performing better [5].”
Extensions of recurrent neural network language model (2011, background): “Table 2 shows comparison of the feedforward [12], simple recurrent [4] and BPTT-trained recurrent neural network language models on two corpora.”