Bridging Fluency Disparity between Native and Nonnative Speakers in Multilingual Multiparty Collaboration Using a Clarification Agent

Abstract

Multiparty collaboration using a common language is often challenging for nonnative speakers (NNS). Conversation can move forward rapidly, with terms and references unfamiliar to NNS often going unexplained because NNS do not request clarification due to cognitive overload or face concerns. Language difficulties may further lead to NNS having a low level of participation in a conversation, which could be a loss for multilingual teams. To help NNS resolve potential confusions due to unfamiliar language use without risking face concerns, we created a conversation agent that asked clarification questions intended to help NNS follow and participate in multiparty conversations. We conducted a within-subjects laboratory experiment with 17 triads of 2 NS and 1 NNS, who performed a series of collaborative tasks under three conditions: a) no agent, b) a high-level agent that resembles a NNS with good command of English, and c) a low-level agent that resembles a NNS with poor English skills. Results suggest that NS made significantly more clarifications in both agent conditions than without an agent. In the high-level agent condition, NNS reported an increase in understanding after the agent's interruption and spoke significantly more. Further, NNS evaluated their communication competence in English highest in the low-level agent condition and lowest in the control condition. Our findings suggest several directions to improve the tool to better facilitate multilingual multiparty communication.

A Taxonomy for Human-LLM Interaction Modes: An Initial Exploration

“…In this approach, LLMs are used to streamline and facilitate the team's interaction process, particularly during the facilitating phase. This is typically achieved by integrating an agent within the team that aids in communication [16], decision-making [83], information sharing, and coordination. By smoothing out the interaction process, the LLM ensures that the team's workflow continues seamlessly and effectively, enhancing collaboration and productivity.…”