Papers by Dung Thai
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models (2024.findings-acl)
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| Challenge: | Existing studies show that fine-tuned textual transformer models are vulnerable to adversarial text perturbations. |
| Approach: | They extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict adversarial robustness of the fine- tuned models. |
| Outcome: | The proposed framework can be used as an additional tool for robustness evaluation since it saves 30x-193x runtime compared to the traditional technique and can be easily used under adversarial training. |
TABBIE: Pretrained Representations of Tabular Data (2021.naacl-main)
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| Challenge: | Existing work on tabular representation-learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. |
| Approach: | They propose a tabular representation-learning model that integrates tabular data with a pretraining objective function that detects corrupted cells. |
| Outcome: | The proposed model understands complex table semantics and numerical trends. |
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering (2022.findings-emnlp)
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Srinivas Ravishankar, Dung Thai, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue
| Challenge: | Existing approaches for Knowledge Base Question Answering focus on a specific knowledge base or evaluating it on underlying knowledge base requires non-trivial changes. |
| Approach: | They propose a framework that separates semantic parsing from knowledge base interaction . they propose KBQA framework that allows generalization across knowledge bases . |
| Outcome: | The proposed framework achieves comparable or state-of-the-art performance on datasets with a different knowledge base. |
Case-based Reasoning for Natural Language Queries over Knowledge Bases (2021.emnlp-main)
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Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum
| Challenge: | Using human-labeled examples, case-based reasoning can solve complex problems from scratch . case-Based reasoning is a paradigm that is used to solve complex problem . |
| Approach: | They propose a neuro-symbolic CBR approach for question answering over large knowledge bases. |
| Outcome: | The proposed approach outperforms the current state of the art on a CWQ dataset by 11% on accuracy. |
Machine Reading Comprehension using Case-based Reasoning (2023.findings-emnlp)
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Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum
| Challenge: | Current state-of-the-art machine readers do not support case-based reasoning . |
| Approach: | They propose a method that extracts a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers. |
| Outcome: | The proposed method outperforms baselines on NaturalQuestions and NewsQA by 11.5 and 8.4 EM. |