When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solution

• URL: http://arxiv.org/abs/2305.05754v1
• Date: Tue, 9 May 2023 20:23:17 GMT
• Title: When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solution
• Authors: Zhengxiang Shi, Jerome Ramos, To Eun Kim, Xi Wang, Hossein A. Rahmani, Aldo Lipani
• Abstract summary: In collaborative tasks, effective communication is crucial for achieving joint goals. We aim to develop an intelligent builder agent to build structures based on user input through dialogue.
• Score: 6.36729066736314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In collaborative tasks, effective communication is crucial for achieving joint goals. One such task is collaborative building where builders must communicate with each other to construct desired structures in a simulated environment such as Minecraft. We aim to develop an intelligent builder agent to build structures based on user input through dialogue. However, in collaborative building, builders may encounter situations that are difficult to interpret based on the available information and instructions, leading to ambiguity. In the NeurIPS 2022 Competition NLP Task, we address two key research questions, with the goal of filling this gap: when should the agent ask for clarification, and what clarification questions should it ask? We move towards this target with two sub-tasks, a classification task and a ranking task. For the classification task, the goal is to determine whether the agent should ask for clarification based on the current world state and dialogue history. For the ranking task, the goal is to rank the relevant clarification questions from a pool of candidates. In this report, we briefly introduce our methods for the classification and ranking task. For the classification task, our model achieves an F1 score of 0.757, which placed the 3rd on the leaderboard. For the ranking task, our model achieves about 0.38 for Mean Reciprocal Rank by extending the traditional ranking model. Lastly, we discuss various neural approaches for the ranking task and future direction.

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