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Register a new userDynamically Addressing Unseen Rumor via Continual Learning
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Added by
Nayeon Lee
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model. Previous works address this issue by improving the models generalizability, with an assumption that the model will stay unchanged even after the new outbreak of an event. In this work, we propose an alternative solution to continuously update the model in accordance with the dynamics of rumor domain creations. The biggest technical challenge associated with this new approach is the catastrophic forgetting of previous learnings due to new learnings. We adopt continual learning strategies that control the new learnings to avoid catastrophic forgetting and propose an additional strategy that can jointly be used to strengthen the forgetting alleviation.
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We present a method for continual learning of speech representations for multiple languages using self-supervised learning (SSL) and applying these for automatic speech recognition. There is an abundance of unannotated speech, so creating self-supervised representations from raw audio and finetuning on a small annotated datasets is a promising direction to build speech recognition systems. Wav2vec models perform SSL on raw audio in a pretraining phase and then finetune on a small fraction of annotated data. SSL models have produced state of the art results for ASR. However, these models are very expensive to pretrain with self-supervision. We tackle the problem of learning new language representations continually from audio without forgetting a previous language representation. We use ideas from continual learning to transfer knowledge from a previous task to speed up pretraining a new language task. Our continual-wav2vec2 model can decrease pretraining times by 32% when learning a new language task, and learn this new audio-language representation without forgetting previous language representation.
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