Our work focuses on the biases that emerge in the natural language generation (NLG) task of sentence completion. In particular, we introduce a mathematical framework of fairness for NLG followed by an evaluation of gender biases in two state-of-the-art language models, GPT-2 and XLNet. Our analysis provides a theoretical formulation for defining biases in NLG and empirical evidence that existing language generation models embed gender bias.

Vast improvements in natural language understanding and speech recognition have paved the way for conversational interaction with computers. While conversational agents have often been used for short goal-oriented dialog, we know little about agents for developing computer programs. To explore the utility of natural language for programming, we conducted a study (n=45) comparing different input methods to a conversational programming system we developed. Participants completed novice and advanced tasks using voice-based, text-based, and voice-or-text-based systems. We found that users appreciated aspects of each system (e.g., voice-input efficiency, text-input precision) and that novice users were more optimistic about programming using voice-input than advanced users. Our results show that future conversational programming tools should be tailored to users' programming experience and allow users to choose their preferred input mode. To reduce cognitive load, future interfaces can incorporate visualizations and possess custom natural language understanding and speech recognition models for programming.

In this paper, revisiting work of Rodriguez-Villegas, we produce infinite families of polynomials that satisfy the essential expected properties of the Riemann zeta-function. We identify natural families of rational functions in x which are the generating functions for the values of “zeta-polynomials” Z_T(s). In analogy with the zeta-function, these polynomials satisfy a functional equation of the form Z_T(s) = (−1)^t Z_T(1 − s), and enjoy the additional property that if Z_T(ρ) = 0, then Re(ρ) = 1/2. Namely, these polynomials satisfy the Riemann Hypothesis.