A Japanese Conversation Dataset for Grounding Referential Expressions in Real-world Interactions

Core Concepts

Multimodal reference resolution is crucial for human-assisting systems to understand user intentions and generate appropriate actions in the real world. The J-CRe3 dataset provides egocentric video, dialogue audio, and comprehensive annotations of textual and text-to-object reference relations to enable research on this task.

Abstract

The authors propose a multimodal reference resolution task and construct a Japanese Conversation dataset for Real-world Reference Resolution (J-CRe3) to address this task. The dataset contains egocentric video and dialogue audio of real-world conversations between a master and an assistant robot, with annotations for various reference relations, including predicate-argument structures, bridging references, and coreferences. The multimodal reference resolution task consists of three subtasks: textual reference resolution, object detection, and text-to-object reference resolution. The authors evaluate an experimental model on these subtasks and find that while textual reference resolution achieves performance comparable to existing monologue datasets, text-to-object reference resolution remains a challenging task with much room for improvement. The dataset contains 93 dialogues with 2,131 utterances, 79,694 bounding boxes, and a large number of zero references, which are crucial for understanding real-world conversations. The authors also discuss the impact of the robot actor's behavior on the model's performance, highlighting the importance of evaluating the model on earlier frames corresponding to the target utterance.

Stats

The dataset contains 93 dialogues with 2,131 utterances, 79,694 bounding boxes, and 7,177 zero references. The number of unique object classes in the dataset is 166. The distinct-1 and distinct-2 scores of the dialogue texts are 0.087 and 0.336, respectively, indicating higher textual diversity compared to the SIMMC 2.1 dataset.

Quotes

"Understanding expressions that refer to the physical world is crucial for such human-assisting systems in the real world, as robots that must perform actions that are expected by users." "Our dataset has two types of reference relations: textual and text-to-object reference relations. Textual reference relations include predicate-argument structures, bridging reference relations, and coreference relations." "The text-to-object reference resolution task is a connection between a noun phrase and the bounding box to which it refers, as in the case of here and sports drink in the example."

Key Insights Distilled From

J-CRe3

by Nobuhiro Ued... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19259.pdf

Deeper Inquiries

How can the performance of text-to-object reference resolution be improved by leveraging the interdependence between textual and visual information

To improve the performance of text-to-object reference resolution by leveraging the interdependence between textual and visual information, a few strategies can be implemented: Cross-Modal Fusion: By integrating textual and visual features at different levels of abstraction, such as at the word, phrase, or sentence level, the model can learn to associate relevant objects with corresponding textual references more effectively. Contextual Embeddings: Utilizing contextual embeddings from pre-trained language models like BERT or RoBERTa can help capture the nuanced relationships between text and objects in the images, enhancing the model's understanding of the context. Attention Mechanisms: Implementing attention mechanisms that dynamically focus on relevant parts of the image based on the textual input can improve the model's ability to ground phrases to the correct objects. Fine-Tuning on Diverse Data: Training the model on a diverse range of data, including different scenarios, environments, and dialogue types, can help the model generalize better and handle a wider variety of reference resolutions.

What other types of real-world interactions, beyond the master-robot scenario, could be explored to further expand the diversity and applicability of the J-CRe3 dataset

Expanding the J-CRe3 dataset to encompass a broader range of real-world interactions beyond the master-robot scenario can significantly enhance its diversity and applicability. Some potential scenarios to explore include: Human-Human Interactions: Incorporating dialogues between two human participants engaging in various activities like cooking, shopping, or collaborative tasks can provide a richer dataset for studying reference resolution in natural conversations. Customer Service Scenarios: Including dialogues between customers and service representatives in different settings such as retail stores, call centers, or hospitality environments can offer insights into resolving references in customer interactions. Educational Settings: Dialogues between teachers and students in classroom settings or online learning platforms can present unique challenges for reference resolution, especially in instructional contexts. Medical Consultations: Conversations between healthcare providers and patients discussing symptoms, treatments, or medical procedures can introduce specialized vocabulary and reference resolution challenges.

How can the dataset be extended to support research on multimodal dialogue systems that can handle more complex and open-ended conversations in real-world settings

To extend the J-CRe3 dataset for research on multimodal dialogue systems handling complex and open-ended conversations in real-world settings, the following approaches can be considered: Longer Conversations: Including dialogues with multiple turns and more extended interactions can help train models to maintain context over extended periods and handle more intricate dialogue structures. Ambiguity and Uncertainty: Introducing scenarios with ambiguous references, conflicting information, or uncertain contexts can enhance the dataset's ability to address real-world conversational challenges. Multi-Party Conversations: Incorporating dialogues involving multiple participants can simulate group discussions, negotiations, or social interactions, providing a more realistic and challenging environment for dialogue systems. Dynamic Environments: Creating scenarios where the environment or objects change over time can test the model's adaptability and ability to track references in evolving situations. By incorporating these elements into the dataset, researchers can explore the capabilities of multimodal dialogue systems in handling the complexities of real-world conversations more effectively.