Translating in real-time, a.k.a. simultaneous translation, outputs
translation words before the input sentence ends, which is a challenging
problem for conventional machine translation methods. We propose a neural
machine translation (NMT) framework for simultaneous translation in which an
agent learns to make decisions on when to translate from the interaction with a
pre-trained NMT environment. To trade off quality and delay, we extensively
explore various targets for delay and design a method for beam-search
applicable in the simultaneous MT setting. Experiments against state-of-the-art
baselines on two language pairs demonstrate the efficacy of the proposed
framework both quantitatively and qualitatively.

本论文提出了一种基于神经机器翻译的实时翻译框架，通过代理决策来实现翻译输出实时化并且具有可控的翻译质量与延迟，通过在两种语言对上的实验结果表明了该方法在定量与定性方面的有效性。

基于神经机器翻译的实时翻译学习

Learning to Translate in Real-time with Neural Machine Translation

Evolutionary game theory is a successful mathematical framework geared
towards understanding the selective pressures that affect the evolution of the
strategies of agents engaged in interactions with potential conflicts. While a
mathematical treatment of the costs and benefits of decisions can predict the
optimal strategy in simple settings, more realistic settings such as finite
populations, non-vanishing mutations rates, stochastic decisions, communication
between agents, and spatial interactions, require agent-based methods where
each agent is modeled as an individual, carries its own genes that determine
its decisions, and where the evolutionary outcome can only be ascertained by
evolving the population of agents forward in time. While highlighting standard
mathematical results, we compare those to agent-based methods that can go
beyond the limitations of equations and simulate the complexity of
heterogeneous populations and an ever-changing set of interactors. We conclude
that agent-based methods can predict evolutionary outcomes where purely
mathematical treatments cannot tread (for example in the weak selection--strong
mutation limit), but that mathematics is crucial to validate the computational
simulations.

这篇论文研究了进化博弈论的数学模型，探讨了在非简单情景下（如有限人口、不消失的突变率、随机决策、沟通等）运用基于个体的方法进行仿真的必要性，并总结了数学模型和计算仿真方法的优缺点。