Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Pushpankar Kumar Pushp
- sl:arxiv_num : 1712.05972
- sl:arxiv_published : 2017-12-16T15:17:07Z
- sl:arxiv_summary : Zero-shot Learners are models capable of predicting unseen classes. In this
work, we propose a Zero-shot Learning approach for text categorization. Our
method involves training model on a large corpus of sentences to learn the
relationship between a sentence and embedding of sentence's tags. Learning such
relationship makes the model generalize to unseen sentences, tags, and even new
datasets provided they can be put into same embedding space. The model learns
to predict whether a given sentence is related to a tag or not; unlike other
classifiers that learn to classify the sentence as one of the possible classes.
We propose three different neural networks for the task and report their
accuracy on the test set of the dataset used for training them as well as two
other standard datasets for which no retraining was done. We show that our
models generalize well across new unseen classes in both cases. Although the
models do not achieve the accuracy level of the state of the art supervised
models, yet it evidently is a step forward towards general intelligence in
natural language processing.@en
- sl:arxiv_title : Train Once, Test Anywhere: Zero-Shot Learning for Text Classification@en
- sl:arxiv_updated : 2017-12-23T20:05:03Z
- sl:bookmarkOf : https://arxiv.org/abs/1712.05972
- sl:creationDate : 2021-10-16
- sl:creationTime : 2021-10-16T13:59:40Z
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