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Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are… Expand Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this… Expand We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word… Expand We present KenLM, a library that implements two data structures for efficient language model queries, reducing both time and… Expand We present several modifications of the original recurrent neural network language model (RNN LM).While this model has been shown… Expand A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results… Expand Neural probabilistic language models (NPLMs) have been shown to be competitive with and occasionally superior to the widely-used… Expand SRILM is a collection of C++ libraries, executable programs, and helper scripts designed to allow both production of and… Expand A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is… Expand Statistical language modeling has been successfully used for speech recognition, part-of-speech tagging, and syntactic parsing… Expand