@inproceedings{jang-choi-2023-multi,
    title = "Multi-Task Learning for Ambiguous Candidate Identification with Pre-trained Model",
    author = "Jang, Daesik  and
      Choi, Hyewon",
    editor = "Chen, Yun-Nung  and
      Crook, Paul  and
      Galley, Michel  and
      Ghazarian, Sarik  and
      Gunasekara, Chulaka  and
      Gupta, Raghav  and
      Hedayatnia, Behnam  and
      Kottur, Satwik  and
      Moon, Seungwhan  and
      Zhang, Chen",
    booktitle = "Proceedings of The Eleventh Dialog System Technology Challenge",
    month = sep,
    year = "2023",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.dstc-1.2",
    pages = "9--14",
    abstract = "Recently, research using multimodal datasets containing image and text information has been conducted actively. One of them is the SIMMC2.1 dataset. It is a more complicated dataset than answering a conversation using only text because it should predict an answer after understanding the relationship between images and text. Therefore, there are limitations to answering a conversation only using text-based models such as BERT or GPT-2, so models with both image and language understanding abilities should be considered. We propose a new model that is effective for the ambiguous candidate identification task in DSTC11 SIMMC2.1 Tark. It consists of a simple pipeline model structure, which has two steps. The first step is to check whether there is ambiguity in the current user utterance, and the second step is to extract objects mentioned in the ambiguous utterance of the user. We suggest a new learning framework with a pre-trained image model and text model that is effective for the ambiguous candidate identification task. Experiments show that the proposed method can improve the model performance, and our model achieved 3rd place in sub-task 1 of the SIMMC2.1 track.",
}