Papers by Vaibhava Goel

2 papers
Unsupervised Adaptation of Question Answering Systems via Generative Self-training (2020.emnlp-main)

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Challenge: Supervised self-training methods have transformed applied machine learning . however, adapting to target data has received little attention .
Approach: They propose a method to generate synthetic QA pairs for unsupervised self adaptation . they use massive amounts of data to simulate self-supervised tasks .
Outcome: The proposed method improves QA systems significantly by using less data and training computation than existing augmentation approaches.
CNNBiF: CNN-based Bigram Features for Named Entity Recognition (2021.findings-emnlp)

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Challenge: Named entity recognition tasks require a self-attention mechanism with unconstrained length that fails to capture local dependencies.
Approach: They propose a joint training objective which better captures the semantics of words corresponding to the same entity by augmenting the objective with a group-consistency loss component.
Outcome: The proposed model achieves a test F1 of 93.98 with a single transformer model.

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