Papers by Tuan Dinh
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering (2020.coling-main)
Copied to clipboard
| Challenge: | Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. |
| Approach: | They propose to design an explainable, evidence-based memory network architecture that connects current sample with seen samples and bases its decision on these samples. |
| Outcome: | The proposed model can trace errors to training instances that might have caused errors . the proposed model achieves state-of-the-art performance on two popular datasets . |
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment (2022.findings-emnlp)
Copied to clipboard
Tuan Dinh, Jy-yong Sohn, Shashank Rajput, Timothy Ossowski, Yifei Ming, Junjie Hu, Dimitris Papailiopoulos, Kangwook Lee
| Challenge: | Recent studies show that unsupervised word translation is more accurate and robust without parallel corpora. |
| Approach: | They propose a method for unsupervised word translation that leverages visual observations and pretrained language-image models to align words. |
| Outcome: | The proposed method improves on the state-of-the-art language-image pretraining method for bilingual word alignment. |