Improving Text Normalization using Character-Blocks Based Models and System Combination
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationText NormalizationSimilar Papers 제목 키워드 기반
Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning
We define multilevel text normalization as sequence-to-sequence processing that transforms naturally noisy text into a sequence of normalized units of meaning (morphemes) in three steps: 1) writing normalization, 2) lemm…
DecoderLanguage ModelingLanguage ModellingLemmatization+1Dynamical Isometry for Residual Networks
The training success, training speed and generalization ability of neural networks rely crucially on the choice of random parameter initialization. It has been shown for multiple architectures that initial dynamical isom…
DiversityRethinking the role of normalization and residual blocks for spiking neural networks
Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption. However, deep SNNs are not easy to train due to the excessive firing of spiking neurons in the hidden lay…
Recurrent Affine Transformation for Text-to-image Synthesis
Text-to-image synthesis aims to generate natural images conditioned on text descriptions. The main difficulty of this task lies in effectively fusing text information into the image synthesis process. Existing methods us…
Text-to-Image GenerationTowards Shared Datasets for Normalization Research
In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets r…
Domain AdaptationLexical NormalizationMachine TranslationOpinion Mining+2