Мощный ансамбль из пяти моделей AWD-LSTM-DOC, оптимизированный для работы с набором данных WikiText-2. Благодаря инновационному методу объединения данных из разных слоев RNN, этот ИИ демонстрирует исключительную точность в задачах языкового моделирования.
This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also from middle layers. Our proposed method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by Yang et al. (2018). The proposed method improves the current state-of-the-art language model and achieves the best score on the Penn Treebank and WikiText-2, which are the standard benchmark datasets. Moreover, we indicate our proposed method contributes to two application tasks: machine translation and headline generation. Our code is publicly available at: this https URL.