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        <title>NLU for Game-based Learning in Real: Initial Evaluations</title>
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        <namePart type="given">Saurav</namePart>
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    <name type="personal">
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            <title>Proceedings of the 9th Workshop on Games and Natural Language Processing within the 13th Language Resources and Evaluation Conference</title>
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            <namePart type="given">Chris</namePart>
            <namePart type="family">Madge</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/2022.games-1.4</url>
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%0 Conference Proceedings
%T NLU for Game-based Learning in Real: Initial Evaluations
%A Okur, Eda
%A Sahay, Saurav
%A Nachman, Lama
%Y Madge, Chris
%S Proceedings of the 9th Workshop on Games and Natural Language Processing within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F okur-etal-2022-nlu
%X 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.
%U https://aclanthology.org/2022.games-1.4
%P 28-39

Markdown (Informal)

[NLU for Game-based Learning in Real: Initial Evaluations](https://aclanthology.org/2022.games-1.4) (Okur et al., games 2022)

• NLU for Game-based Learning in Real: Initial Evaluations (Okur et al., games 2022)

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.