no code implementations • 28 Mar 2024 • Adithya Kulkarni, Oliver Eulenstein, Qi Li
Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks.
no code implementations • 11 Nov 2023 • Mohna Chakraborty, Adithya Kulkarni, Qi Li
(2) What are different prompts performance using ChatGPT for fact verification tasks?
1 code implementation • 25 May 2023 • Mohna Chakraborty, Adithya Kulkarni, Qi Li
We empirically demonstrate that the top-ranked prompts are high-quality and significantly outperform the base prompt and the prompts generated using few-shot learning for the binary sentence-level sentiment classification task.
1 code implementation • 19 Jan 2022 • Adithya Kulkarni, Nasim Sabetpour, Alexey Markin, Oliver Eulenstein, Qi Li
This paper adopts the truth discovery idea to aggregate constituency parse trees from different parsers by estimating their reliability in the absence of ground truth.
1 code implementation • 9 Sep 2021 • Nasim Sabetpour, Adithya Kulkarni, Sihong Xie, Qi Li
The proposed Aggregation method for Sequential Labels from Crowds ($AggSLC$) jointly considers the characteristics of sequential labeling tasks, workers' reliabilities, and advanced machine learning techniques.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Nasim Sabetpour, Adithya Kulkarni, Qi Li
Our results show that the proposed OptSLA outperforms the state-of-the-art aggregation methods, and the results are easier to interpret.