Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (1907.01166v1)

Abstract: Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to obtain semantic information; and (2) a dialogue agent must perceive and process information from different modalities (audio, video, caption, etc.) to obtain a comprehensive understanding. Most existing work is based on RNNs and sequence-to-sequence architectures, which are not very effective for capturing complex long-term dependencies (like in videos). To overcome this, we propose Multimodal Transformer Networks (MTN) to encode videos and incorporate information from different modalities. We also propose query-aware attention through an auto-encoder to extract query-aware features from non-text modalities. We develop a training procedure to simulate token-level decoding to improve the quality of generated responses during inference. We get state of the art performance on Dialogue System Technology Challenge 7 (DSTC7). Our model also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance. We implemented our models using PyTorch and the code is released at https://github.com/henryhungle/MTN.

Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems

The paper "Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems" introduces a novel approach to tackling the complex problem of generating dialogue responses that are grounded in video content. The task, defined as Video-Grounded Dialogue Systems (VGDS), requires not only processing textual and visual data but also understanding and integrating multimodal information from video frames and audio streams. This paper proposes an innovative architecture based on Multimodal Transformer Networks (MTN) to efficiently handle and synthesize large-scale multimodal data for dialogue generation.

Core Contributions

To address the intricacies associated with VGDS, the authors explore a transformer-based model that leverages the powerful attention mechanisms to process multiple input modalities, unlike traditional RNNs and sequence-to-sequence models that have shown limitations in capturing long-term dependencies typical in video data.

1. Multimodal Transformer Network (MTN): The paper proposes MTN, which extends the transformer architecture by encoding videos and managing information from diverse modalities, effectively employing multi-head attention layers to process video frames across visual, audio, and caption features.
2. Query-Aware Attention via Auto-Encoder: A novel use of a query-aware attention mechanism through an auto-encoder is introduced to enhance feature extraction from non-textual inputs such as video and audio. This component facilitates the model's ability to reason over complex input data.
3. Simulated Token-Level Decoding: A unique training procedure is developed to emulate token-level decoding, which aims to bridge the discrepancy between training and inference, enhancing the generated responses' quality.

Evaluation and Results

The proposed MTN model demonstrates state-of-the-art performance on the Dialogue System Technology Challenge 7 dataset, surpassing previous models across multiple evaluation metrics including BLEU, CIDEr, METEOR, and ROUGE-L. Notably, with significant improvements in BLEU4 and CIDEr scores, the MTN model effectively captures and utilizes the contextual nuances within video-grounded dialogues. Additionally, the MTN approach extends its application to visual-grounded dialogue tasks, where it shows promising adaptability.

Implications and Future Directions

The MTN's ability to leverage the transformer model architecture for handling multiple data modalities presents significant implications for advancing multimodal dialogue systems. The architecture aligns well with current trends in employing attention-based models for complex sequence processing tasks. The adoption of query-aware attention and token-level simulation mechanisms in MTN sets a precedent for future studies aiming to enhance contextual learning in complex dialogue systems.

Future research could explore integrating pre-trained models like BERT or similar architectures to further enhance semantic understanding within dialogue contexts. Moreover, expanding the scope of multimodal data, including more diverse audiovisual datasets, could provide broader insights into MTN's applicability.

In conclusion, the paper offers important conceptual and practical advancements for researchers working on video-grounded dialogue systems, presenting a robust framework capable of comprehensive multimodal reasoning that could inspire further exploration and development in this area.