Papers by Udit Arora
Types of Out-of-Distribution Texts and How to Detect Them (2021.emnlp-main)
Copied to clipboard
| Challenge: | Current NLP models produce unreliable or catastrophic predictions when training and test distributions differ . current models tend to produce unreliability or even catastrophic predictions that hurt user trust. |
| Approach: | They categorize examples as exhibiting a background shift or semantic shift and use calibration and density estimation methods to detect OOD examples. |
| Outcome: | The proposed methods beat calibration methods in background shift settings and perform worse in semantic shift settings. |
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization (2023.acl-long)
Copied to clipboard
| Challenge: | Besides digital archiving of memes and their metadata, there is no efficient way to deduce a meme’s context dynamically. |
| Approach: | They propose a task to mine the context that succinctly explains the background of a meme and a related document to capture cross-modal semantic dependencies between the meme and the context. |
| Outcome: | The proposed dataset outperforms existing systems and shows that it can capture cross-modal semantic dependencies between the meme and the context. |