Margaret Mitchell

TL;DR: This paper used a neural network architecture to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances and showed consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.

TL;DR: This article introduced Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs, and showed that deltaBLEU correlates reasonably with human judgments and outperforms sentence-level and IBM bLEU in terms of Spearman's rho and Kendall's tau.

TL;DR: An approach to face attribute detection that retains or improves attribute detection accuracy across gender and race subgroups by learning demographic information prior to learning the attribute detection task is demonstrated.

TL;DR: In this paper, the authors demonstrate a set of five ethical concerns in the particular case of auditing commercial facial processing technology, highlighting additional design considerations and ethical tensions the auditor needs to be aware of so as not exacerbate or complement the harms propagated by the audited system.

TL;DR: A generic evaluation framework, Perturbation Sensitivity Analysis, is proposed, which detects unintended model biases related to named entities, and requires no new annotations or corpora to be employed.