Explicit Word Density Estimation for Language Modelling

Autor:	Andonov, Jovan, Ganea, Octavian, Grnarova, Paulina, Bécigneul, Gary, Hofmann, Thomas
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Language Modelling has been a central part of Natural Language Processing for a very long time and in the past few years LSTM-based language models have been the go-to method for commercial language modeling. Recently, it has been shown that when looking at language modelling from a matrix factorization point of view, the final Softmax layer limits the expressiveness of the model, by putting an upper bound on the rank of the resulting matrix. Additionally, a new family of neural networks based called NeuralODEs, has been introduced as a continuous alternative to Residual Networks. Moreover, it has been shown that there is a connection between these models and Normalizing Flows. In this work we propose a new family of language models based on NeuralODEs and the continuous analogue of Normalizing Flows and manage to improve on some of the baselines. Comment: Master's thesis
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.10256 Zobrazit plný text záznamu View this record from Arxiv