Arshiya Aggarwal — accepted AI-conference papers

2022

Towards Robust NLG Bias Evaluation with Syntactically-diverse Prompts

EMNLP 2022finding

We present a robust methodology for evaluating biases in natural language generation(NLG) systems. Previous works use fixed hand-crafted prefix templates with mentions of various demographic groups to prompt models to generate continuations for bias analysis. These fixed prefix templates could thems…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗

2021

An Empirical Investigation of Bias in the Multimodal Analysis of Financial Earnings Calls

Ramit Sawhney, Arshiya Aggarwal, Rajiv Ratn Shah

NAACL 2021long

Volatility prediction is complex due to the stock market’s stochastic nature. Existing research focuses on the textual elements of financial disclosures like earnings calls transcripts to forecast stock volatility and risk, but ignores the rich acoustic features in the company executives’ speech. Re…

Cited by 22 Source ↗PDF ↗code ↗Scholar ↗