Unimodal or Multimodal? : A Case Study For Intent Detection Task
Authors: Ankan Mullick, Saransh Sharma, Abhik Jana, Pawan Goyal


Reviewer paper summaries: This paper investigated the multimodal intent detection tasks through:
1. comparing the importance of various modalities under the two multimodal intent detection datasets, MIntRec-1 and MIntRec2.0.
2. assessing the strengths and weakness of unimodal versus multimodal architectures (as well as LLMs versus non-LLMs) on intent detection task
Summary of strengths: 1. The paper conducted thorough experiments for well-categorized models on the multimodal intent detection datasets.
2. The paper did a comprehensive analysis of various topics considering the effectiveness of various models over the different intent categories, the importance of different modalities, the performance of models under limited resources, and error analysis.
Summary of weaknesses: 1. While the paper thoroughly analyzed the various models on the two multimodal intent detection datasets, the paper should provide more insights into what a better direction would be for such a task.
2. As seen in Figure 3 WordCloud of Agree, Apologise, and Thank labels, some categories are highly related to some name (e.g. Judy, Amy, Cheyenne in the apologize category). There could be more clarification on whether the intents are categorized correctly based on the actual content, or certain bias from the dataset.
Comments, suggestions and typos: 1. While it is useful to compare the models on multimodal intent detection tasks, it would also be beneficial to compare both the unimodal and multimodal models on unimodal intent detection tasks to verify the effectiveness of processing the textual information using the multimodal models. Moreover, further analysis can be conducted by comparing the importance of visual and audio information.
2. There can be more comparison of the computation resources (e.g. learnable parameters), which is also an important factor when thinking about model selection for intent detection tasks.
Overall assessment scores: 3.5