47 research outputs found
Multitask Learning for Low Resource Spoken Language Understanding
We explore the benefits that multitask learning offer to speech processing as
we train models on dual objectives with automatic speech recognition and intent
classification or sentiment classification. Our models, although being of
modest size, show improvements over models trained end-to-end on intent
classification. We compare different settings to find the optimal disposition
of each task module compared to one another. Finally, we study the performance
of the models in low-resource scenario by training the models with as few as
one example per class. We show that multitask learning in these scenarios
compete with a baseline model trained on text features and performs
considerably better than a pipeline model. On sentiment classification, we
match the performance of an end-to-end model with ten times as many parameters.
We consider 4 tasks and 4 datasets in Dutch and English
A Data Efficient End-To-End Spoken Language Understanding Architecture
End-to-end architectures have been recently proposed for spoken language
understanding (SLU) and semantic parsing. Based on a large amount of data,
those models learn jointly acoustic and linguistic-sequential features. Such
architectures give very good results in the context of domain, intent and slot
detection, their application in a more complex semantic chunking and tagging
task is less easy. For that, in many cases, models are combined with an
external language model to enhance their performance.
In this paper we introduce a data efficient system which is trained
end-to-end, with no additional, pre-trained external module. One key feature of
our approach is an incremental training procedure where acoustic, language and
semantic models are trained sequentially one after the other. The proposed
model has a reasonable size and achieves competitive results with respect to
state-of-the-art while using a small training dataset. In particular, we reach
24.02% Concept Error Rate (CER) on MEDIA/test while training on MEDIA/train
without any additional data.Comment: Accepted to ICASSP 202
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
Behavioral changes are the earliest signs of a mental disorder, but arguably,
the dynamics of brain function gets affected even earlier. Subsequently,
spatio-temporal structure of disorder-specific dynamics is crucial for early
diagnosis and understanding the disorder mechanism. A common way of learning
discriminatory features relies on training a classifier and evaluating feature
importance. Classical classifiers, based on handcrafted features are quite
powerful, but suffer the curse of dimensionality when applied to large input
dimensions of spatio-temporal data. Deep learning algorithms could handle the
problem and a model introspection could highlight discriminatory
spatio-temporal regions but need way more samples to train. In this paper we
present a novel self supervised training schema which reinforces whole sequence
mutual information local to context (whole MILC). We pre-train the whole MILC
model on unlabeled and unrelated healthy control data. We test our model on
three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers
and four different studies. Our algorithm outperforms existing self-supervised
pre-training methods and provides competitive classification results to
classical machine learning algorithms. Importantly, whole MILC enables
attribution of subject diagnosis to specific spatio-temporal regions in the
fMRI signal.Comment: Accepted at MICCAI 2020. arXiv admin note: substantial text overlap
with arXiv:1912.0313