Papers by Sai Chetan Chinthakindi
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization (2021.findings-emnlp)
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| Challenge: | Existing approaches to improve generalization of neural models use a small component of the gradient for maximizing dot-product between batches. |
| Approach: | They propose to use a finite differences first-order algorithm to calculate a gradient from dot-product of gradients and regularize it. |
| Outcome: | The proposed method outperforms previous approaches of Reptile and MAML when used as a regularization technique. |
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge (2022.emnlp-main)
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Revanth Gangi Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi Fung, Kathryn Conger, Ahmed ELsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji
| Challenge: | Current claims detection methods focus on sentence analysis, ignoring other attributes . a key element of identifying misinformation is detecting the claims and the arguments that have been presented. |
| Approach: | They propose a benchmark for attribute-aware claim detection in the news domain . they extend the problem to include extraction of additional attributes related to each claim . |
| Outcome: | The proposed system performs well on the test, but human performance is still poor. |
Learning to Generate Questions by Learning to Recover Answer-containing Sentences (2021.findings-acl)
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| Challenge: | Recent research has focused on synthetically generating a question from a given context and an annotated answer by training an additional generative model. |
| Approach: | They propose a method that learns to generate contextually rich questions by recovering answer-containing sentences. |
| Outcome: | The proposed approach improves the quality and accuracy of existing models and achieves comparable results to the state-of-the-art on MS MARCO and NewsQA. |
A Zero-Shot Claim Detection Framework Using Question Answering (2022.coling-1)
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| Challenge: | Existing claims detection frameworks are portability to emerging events and low-resource training data settings. |
| Approach: | They propose a claim detection framework that leverages zero-shot Question Answering to solve sub-tasks such as topic filtering, claim object detection, and claimer detection. |
| Outcome: | The proposed framework outperforms baselines on the NewsClaims benchmark. |
Keep Learning: Self-supervised Meta-learning for Learning from Inference (2021.eacl-main)
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| Challenge: | A common approach to improve performance of machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks. |
| Approach: | They propose to use model's own class-balanced predictions to back-propagate the loss from the model''s class-balancing predictions (pseudo-labels) this method improves performance of standard backbones such as BERT, Electra, and ResNet-50 on a wide variety of tasks, including question answering on SQuAD and NewsQA . |
| Outcome: | The proposed method outperforms previous approaches on a wide variety of tasks including question answering on SQuAD and NewsQA, benchmark task SuperGLUE, conversation response selection on Ubuntu Dialog corpus v2.0, and image classification on MNIST and ImageNet. |