The Rust language was created to provide safe low-level systems programming. There is both industrial and academic interest in the problem of (semi-)automatically translating C code to Rust in order to exploit Rust's safety guarantees. We study the effectiveness and limitations of existing techniques for automatically translating unsafe raw pointers (in Rust programs translated from C) into safe Rust references via ownership and lifetime inference. Our novel evaluation methodology enables our study to extend beyond prior studies, and to discover new information contradicting the conclusions of prior studies. We find that existing translation methods are severely limited by a lack of precision in the Rust compiler's safety checker, causing many safe pointer manipulations to be labeled as potentially unsafe. Leveraging this information, we propose methods for improving translation, based on encoding the results of a more precise analysis in a manner that is understandable to an unmodified Rust compiler. We implement one of our proposed methods, increasing the number of pointers that can be translated to safe Rust references by 75% over the baseline (from 12% to 21% of all pointers).

Women are often perceived as junior to their male counterparts, even within the same job titles. While there has been significant progress in the evaluation of gender bias in natural language processing (NLP), existing studies seldom investigate how biases toward gender groups change when compounded with other societal biases. In this work, we investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models by introducing a novel framework for probing compound bias. We contribute a benchmark robustness-testing dataset spanning two domains, U.S. senatorship and professorship, created using a distant-supervision method. Our dataset includes human-written text with underlying ground truth and paired counterfactuals. We then examine GPT-2 perplexity and the frequency of gendered language in generated text. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.

Sophie Groenwold, Lily Ou, Aesha Parekh, Samhita Honnavalli, Sharon Levy, Diba Mirza, William Yang Wang. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.