@inproceedings{otani-etal-2023-underspecification,
    title = "On the Underspecification of Situations in Open-domain Conversational Datasets",
    author = "Otani, Naoki  and
      Araki, Jun  and
      Kim, HyeongSik  and
      Hovy, Eduard",
    editor = "Chen, Yun-Nung  and
      Rastogi, Abhinav",
    booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.nlp4convai-1.2",
    doi = "10.18653/v1/2023.nlp4convai-1.2",
    pages = "12--28",
    abstract = "Advances of open-domain conversational systems have been achieved through the creation of numerous conversation datasets. However, many of the commonly used datasets contain little or no information about the conversational situation, such as relevant objects/people, their properties, and relationships. This absence leads to underspecification of the problem space and typically results in undesired dialogue system behavior. This position paper discusses the current state of the field associated with processing situational information. An analysis of response generation using three datasets shows that explicitly provided situational information can improve the coherence and specificity of generated responses, but further experiments reveal that generation systems can be misled by irrelevant information. Our conclusions from this evaluation provide insights into the problem and directions for future research.",
}

Advances of open-domain conversational systems have been achieved through the creation of numerous conversation datasets. However, many of the commonly used datasets contain little or no information about the conversational situation, such as relevant objects/people, their properties, and relationships. This absence leads to underspecification of the problem space and typically results in undesired dialogue system behavior. This position paper discusses the current state of the field associated with processing situational information. An analysis of response generation using three datasets shows that explicitly provided situational information can improve the coherence and specificity of generated responses, but further experiments reveal that generation systems can be misled by irrelevant information. Our conclusions from this evaluation provide insights into the problem and directions for future research.
Otani, Naoki Araki, Jun Kim, HyeongSik Hovy, Eduard
On the Underspecification of Situations in Open-domain Conversational Datasets