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

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

Paula Chocron, Paolo Pareti

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 159-165. https://doi.org/10.24963/ijcai.2018/22

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 tasksĀ  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.
Keywords:
Agent-based and Multi-agent Systems: Agent Communication
Agent-based and Multi-agent Systems: Coordination and Cooperation
Multidisciplinary Topics and Applications: AI and the Web