Natural language (as opposed to structured communication modes such as Morse code) is by far the most common mode of communication between humans, and can thus provide significant insight into both individual mental states and interpersonal dynamics. As part of DARPA’s Artificial Social Intelligence for Successful Teams (ASIST) program, we are developing an AI agent team member that constructs and maintains models of their human teammates and provides appropriate task-relevant advice to improve team processes and mission performance. One of the key components of this agent is a module that uses a rule-based approach to extract task-relevant events from natural language utterances in real time, and publish them for consumption by downstream components. In this case study, we evaluate the performance of our rule-based event extraction system on a recently conducted ASIST experiment consisting of a simulated urban search and rescue mission in Minecraft. We compare the performance of our approach with that of a zero-shot neural classifier, and find that our approach outperforms the classifier for all event types, even when the classifier is used in an oracle setting where it knows how many events should be extracted from each utterance.

The historical comparative method has a long history in historical linguists. It describes a process by which historical linguists aim to reverse-engineer the historical developments of language families in order to reconstruct proto-forms and familial relations between languages. In recent years, there have been multiple attempts to replicate this process through machine learning, especially in the realm of cognate detection (List et al., 2016; Ciobanu and Dinu, 2014; Rama et al., 2018). So far, most of these experiments aimed at actual reconstruction have attempted the prediction of a proto-form from the forms of the daughter languages (Ciobanu and Dinu, 2018; Meloni et al., 2019).. Here, we propose a reimplementation that uses modern related languages, or sisters, instead, to reconstruct the vocabulary of a target language. In particular, we show that we can reconstruct vocabulary of a target language by using a fairly small data set of parallel cognates from different sister languages, using a neural machine translation (NMT) architecture with a standard encoder-decoder setup. This effort is directly in furtherance of the goal to use machine learning tools to help under-served language communities in their efforts at reclaiming, preserving, or reconstructing their own languages.