1 code implementation • COLING (CogALex) 2020 • Eve Fleisig
Language transfer can facilitate learning L2 words whose form and meaning are similar to L1 words, or hinder speakers when the languages differ.
no code implementations • 8 Nov 2023 • Naomi Saphra, Eve Fleisig, Kyunghyun Cho, Adam Lopez
Many NLP researchers are experiencing an existential crisis triggered by the astonishing success of ChatGPT and other systems based on large language models (LLMs).
no code implementations • 6 Nov 2023 • Olivia Huang, Eve Fleisig, Dan Klein
Current practices regarding data collection for natural language processing on Amazon Mechanical Turk (MTurk) often rely on a combination of studies on data quality and heuristics shared among NLP researchers.
1 code implementation • Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics 2023 • Eve Fleisig, Aubrie Amstutz, Chad Atalla, Su Lin Blodgett, Hal Daumé III, Alexandra Olteanu, Emily Sheng, Dan Vann, Hanna Wallach
It is critical to measure and mitigate fairness- related harms caused by AI text generation systems, including stereotyping and demeaning harms.
1 code implementation • 24 May 2023 • Vivek Verma, Eve Fleisig, Nicholas Tomlin, Dan Klein
In conjunction with our model, we release three new datasets of human- and AI-generated text as detection benchmarks in the domains of student essays, creative writing, and news articles.
no code implementations • 24 May 2023 • Vyoma Raman, Eve Fleisig, Dan Klein
We also find text and demographic outliers to be particularly susceptible to errors in the classification of severe toxicity and identity attacks.
no code implementations • 11 May 2023 • Eve Fleisig, Rediet Abebe, Dan Klein
Thus, a crucial problem in hate speech detection is determining whether a statement is offensive to the demographic group that it targets, when that group may constitute a small fraction of the annotator pool.
no code implementations • 20 Mar 2022 • Eve Fleisig, Christiane Fellbaum
Machine translation and other NLP systems often contain significant biases regarding sensitive attributes, such as gender or race, that worsen system performance and perpetuate harmful stereotypes.
no code implementations • 5 Oct 2020 • Ameet Deshpande, Eve Fleisig
Furthermore, this can enable reinforcement learning without rewards, in which the agent learns entirely from these intrinsic sentiment rewards.