Papers by Paras Jain
Contrastive Code Representation Learning (2021.emnlp-main)
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| Challenge: | Recent work learns contextual representations of source code by reconstructing tokens from their context. |
| Approach: | They propose a contrastive pre-training task that learns code functionality, not form . they propose scalable compilers that can generate variants of a program . |
| Outcome: | The proposed task outperforms RoBERTa on an adversarial code clone detection benchmark by 39% AUROC. |
Grounded Graph Decoding improves Compositional Generalization in Question Answering (2021.findings-emnlp)
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| Challenge: | Current compositional generalization models lose syntax context when learning a flat input . a new method to improve compositional globalization is proposed to ground structured predictions with an attention mechanism. |
| Approach: | They propose a method to ground structured predictions by a structure-based attention mechanism. |
| Outcome: | The proposed method performs competitively on the Compositional Freebase Questions dataset. |