Exponential language modeling using morphological features and multi-task learning
For languages with fast vocabulary growth and limited resources, data sparsity leads to challenges in training a language model. One strategy for addressing this problem is to leverage morphological structure as features in the model. This paper explores different uses of unsupervised morphological...
Hlavní autoři: | Fang, H, Ostendorf, M, Baumann, P, Pierrehumbert, J |
---|---|
Médium: | Journal article |
Vydáno: |
Institute of Electrical and Electronics Engineers
2015
|
Podobné jednotky
-
Using pronunciation-based morphological subword units to improve OOV handling in keyword search
Autor: He, Y, a další
Vydáno: (2015) -
Binary Classification with a Pseudo Exponential Model and Its Application for Multi-Task Learning
Autor: Takashi Takenouchi, a další
Vydáno: (2015-08-01) -
DagoBERT: generating derivational morphology with a pretrained language model
Autor: Hofmann, V, a další
Vydáno: (2020) -
Subword-based modeling for handling OOV words inkeyword spotting
Autor: Yanzhang, H, a další
Vydáno: (2014) -
Exploring a radically new exponential Retinex model for multi-task environments
Autor: Ziaur Rahman, a další
Vydáno: (2023-07-01)