Abstract
Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings. Spoken Dialogue Systems (SDS) are critical for these interactive agents to carry out effective goal-oriented communication with users in real-time. For the real-world (i.e., in-the-wild) deployment of such conversational agents, improving the Natural Language Understanding (NLU) module of the goal-oriented SDS pipeline is crucial, especially with limited task-specific datasets. This study explores the potential benefits of a recently proposed transformer-based multi-task NLU architecture, mainly to perform Intent Recognition on small-size domain-specific educational game datasets. The evaluation datasets were collected from children practicing basic math concepts via play-based interactions in game-based learning settings. We investigate the NLU performances on the initial proof-of-concept game datasets versus the real-world deployment datasets and observe anticipated performance drops in-the-wild. We have shown that compared to the more straightforward baseline approaches, Dual Intent and Entity Transformer (DIET) architecture is robust enough to handle real-world data to a large extent for the Intent Recognition task on these domain-specific in-the-wild game datasets.- Anthology ID:
- 2022.games-1.4
- Volume:
- Proceedings of the 9th Workshop on Games and Natural Language Processing within the 13th Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editor:
- Chris Madge
- Venue:
- games
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 28–39
- Language:
- URL:
- https://aclanthology.org/2022.games-1.4
- DOI:
- Cite (ACL):
- Eda Okur, Saurav Sahay, and Lama Nachman. 2022. NLU for Game-based Learning in Real: Initial Evaluations. In Proceedings of the 9th Workshop on Games and Natural Language Processing within the 13th Language Resources and Evaluation Conference, pages 28–39, Marseille, France. European Language Resources Association.
- Cite (Informal):
- NLU for Game-based Learning in Real: Initial Evaluations (Okur et al., games 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.games-1.4.pdf