Zhijing Jin

Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

2019 · arXiv (Cornell University)

Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these …

Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Languages are continuously undergoing changes, and the mechanisms that underlie these changes are still a matter of debate. In this work, we approach language evolution through the lens of causality in order to model not …

When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP

2023 · SSRN Electronic Journal

Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work – sometimes negative transfer occurs between tasks, especially when aggregating loosely related …