Vocabulary Alignment for Collaborative Agents: a Study with Real-World Multilingual How-to Instructions

Collaboration between heterogeneous agents typically requires the ability to communicate meaningfully. This can be challenging in open environments where participants may use different languages. Previous work proposed a technique to infer alignments between different vocabularies that uses only information about the tasks being executed, without any external resource. Until now, this approach has only been evaluated with artificially created data. We adapt this technique to protocols written by humans in natural language, which we extract from instructional webpages. In doing so, we show how to take into account challenges that arise when working with natural language labels. The quality of the alignments obtained with our technique is evaluated in terms of their effectiveness in enabling successful collaborations, using a translation dictionary as a baseline. We show how our technique outperforms the dictionary when used to interact.