Multi-modal Dialogue Generation

• Multi-modal dialogue generation is an approach that synthesizes textual, visual, and audio data to produce contextually grounded conversational responses.
• It leverages architectures like hierarchical encoders, Transformers, and diffusion models to align multi-modal inputs and generate coherent outputs.
• Research in this field focuses on improving context modeling, intent prediction, data curation, and evaluation metrics to enhance dialogue realism and engagement.

Multi-modal dialogue generation refers to the automated production of conversational responses conditioned on multiple modalities—typically text and time-varying visual, audio, or other contextual inputs—enabling systems to generate utterances or actions that are deeply grounded in the observed non-textual environment. Modern research in this domain has focused on next-turn generation, retrieval, and interactive content synthesis over complex, real-world conversational scenarios. Key challenges span vision–language representation learning, context modeling, dataset curation, cross-modal knowledge grounding, and evaluation of both language and generated media. Rigorous benchmarking and model architectures have been developed for text–image, audio–visual, and video-grounded interaction, with increasing integration of large language/vision models, knowledge graphs, and multi-modal fusion techniques.

1. Formal Definitions and Problem Taxonomy

Multi-modal dialogue generation is formulated as the task of generating the $t$-th conversational utterance $u_t$ given a history of prior utterances $u_{<t}$ and corresponding multi-modal context, e.g., images $v_{≤t}$, audio snippets $a_{≤t}$, and/or external knowledge $k_{≤t}$:

$P(u_t\mid u_{<t}, v_{≤t}, a_{≤t}, k_{≤t}).$

For video-based settings, the problem expands to leveraging temporally contiguous frames $V = \{v_1, …, v_m\}$ and synchronized audio $A = \{a_1, …, a_p\}$ to condition each reply. Open-domain scenarios further require asynchronous multi-modal context switches, intent prediction (text, image, video or stop), and dialogue act recognition (Wang et al., 2021, Lin et al., 2023, Feng et al., 2022, Pang et al., 2 Dec 2025).

The core components are:

• Utterance Generation: Predicting text sequence given multi-modal context.
• Media Generation: Producing images, video, or audio as dialogue actions (e.g., photo-sharing).
• Retrieval: Selecting contextually relevant images, video segments, or external knowledge.
• Intent Prediction: Determining the appropriate output modality at each turn.

2. Architectures, Cross-modal Fusion, and Mutual Dependency

Canonical architectures span hierarchical recurrent encoder–decoders, Transformer-based models, diffusion models, and modular agent-based systems:

• Early Models (HRED and Variants): Hierarchical RNNs with dual encoders for text and image features, concatenating high-level utterance and visual state vectors into a context GRU (Agarwal et al., 2018). Visual features are typically pretrained CNN-based embeddings (e.g., VGG-19).
• Transformer-based Approaches: Self-attention over joint sequences of textual and visual tokens enables modeling of long-range dependencies and fine-grained cross-modal alignment. For example, concatenating token embeddings and object-level visual features enables the model to reason about specific scene objects (Wang et al., 2021, Guo et al., 2024).
• Fusion Techniques: Methods include token-level addition or concatenation of visual and text embeddings, gated multi-head attention, and context-aware cross-attentions. Mutual dependency is enforced via joint objectives:

$$\mathcal{L} = \mathcal{L}_{\text{text}} + \lambda \mathcal{L}_{\text{vis}},$$

where $\mathcal{L}_{\text{vis}}$ encourages reconstructibility or identification of visual context from generated utterances (Wang et al., 2021).

• End-to-End Image/Text Generation Chains: Recent models integrate LLMs, Q-Former visual encoders, and diffusion-based image generators into a single gradient-propagating architecture, bypassing traditional caption bridging and enabling robust text and image generation (Guo et al., 2024).
• Audio-Visual and Video Dialogue: Systems include specialized video encoders (e.g., I3D, ViT), Q-Formers for spatiotemporal feature extraction, and fusion with audio descriptors. State-of-the-art video dialogue agents use modular planners ("Conductor") and generators ("Creator") to decompose high-level communicative intent into synchronized, modality-specific outputs (Pang et al., 2 Dec 2025, Wang et al., 31 Jan 2025).

3. Datasets and Benchmarking

The availability and diversity of datasets underpin progress:

• Image–Text Dialog Datasets: DialogCC provides 83k dialogues with 129k unique images and automated, LLM+CLIP-filtered image–utterance alignments—outperforming prior corpora in diversity and curation quality (Lee et al., 2022). MMDialog contains over 1M dialogues and 1.5M unique images across 4k+ open-domain topics (Feng et al., 2022).
• Video-based Dialogue: TikTalk includes 38k videos with 367k spontaneous multi-modal text dialogues, providing rich context via video, audio, and knowledge graph signals (Lin et al., 2023).
• Audio-Visual Dialogues: MAViD’s benchmarks integrate long-duration audio-visual sequences and support synchronized audio/video response evaluation (Pang et al., 2 Dec 2025).
• Photo-Sharing and Multi-media Interaction: Datasets such as PhotoChat, DialogCC, and the Multi-modal Dialogue Benchmark (DialogBen) focus on Photo-sharing and multi-turn, bilingual text-to-image interactive scenarios (Huang et al., 2024, Guo et al., 2024).

Benchmarks utilize a range of automatic metrics: BLEU-n, ROUGE-L, METEOR, CIDEr, Inception Score (IS), Fréchet Inception Distance (FID), MM-Relevance (multi-modal CLIP-based soft F1), as well as task-specific accuracy (e.g., modality switching, slot-filling) and user studies.

Dataset	Modalities	Size (Dialogs)	Avg. Images/Dialogue	Human Filtration	Distinctive Features
DialogCC	Text, Image	83k	7.34	Automated (LLM)	Uniform image spread, strong zero-shot alignment
MMDialog	Text, Image	1.08M	2.59	Filtered	4k+ topics, large scale
TikTalk	Text, Video, Audio	367k	N/A	Semi-automatic	Video, audio, knowledge
MAViD	Text, Video, Audio	1M+	N/A	N/A	Long-form AV generation

4. Knowledge Grounding, Context Modeling, and Slot Conditioning

• Knowledge Integration: Task-oriented multi-modal dialogue often utilizes external knowledge bases (KBs) comprising both attribute-value pairs and relation graphs. Dual knowledge selection matches textual and visual context to relevant KB entries using entity name matching and CLIP-based visual similarity (Chen et al., 2023, Chen et al., 2022). Composed representations fuse shallow token-level and deep relation-level knowledge, with semantic-level regularization distilling target response semantics.
• Slot Attention and Semantic Constraints: End-to-end frameworks include slot-attention mechanisms operating over both text and visual encodings, enabling semantic slot extraction and corresponding slot-conditioned response generation (Firdaus et al., 2023). Representation-level regularization further enhances alignment between composed and ground-truth semantics.
• Scene Awareness and Structured Prompts: Scene- and session-aware joint multi-task training, augmented with zero-shot visual captioning and templated prompt engineering, improves grounding in temporally structured video and movie dialogue (Li et al., 2022).

5. Media Generation, Alignment, and End-to-End Approaches

• Modality Extension: Multi-modal models now support conditional generation of both text and high-fidelity images or audio–video responses. For photo-sharing, state-of-the-art models bypass error-prone pipeline steps (captioning, discrete image-token mapping) via dynamic vocabulary transformation matrices, Gumbel-Softmax, and true end-to-end gradient propagation linking LLMs with diffusion image generators (Guo et al., 2024).
• Video Dialogue Synthesis: TV-Dialogue employs modular multi-agent frameworks for theme-aligned, visually consistent dialogue composition. Sub-agents are responsible for role-specific dialogue turn generation conditioned on visual, emotional, and behavioral states per frame, with a central agent enforcing global thematic and scenario coherence (Wang et al., 31 Jan 2025).
• Audio-Visual Dialogue Generation: The Conductor–Creator split enables fine-grained division of communicative intent (speech vs. motion), with synchronous autoregressive audio and diffusion-based video generation, tightly integrated via recursive cross-attention (Pang et al., 2 Dec 2025).

6. Evaluation Protocols and Model Performance

Evaluation combines automatic metrics with adversarial and human assessments:

• Standard Metrics: BLEU-n, ROUGE-n, METEOR, CIDEr for text; IS, FID for image; Recall@K for retrieval, MM-Relevance for multi-modal response alignment.
• Downstream Performance: Quality improvements from visual feature integration, fine-grained object representation, and mutual text–visual dependency are all statistically significant (e.g., BLEU-4 improvement from 0.95 to 1.22 and Dis-4 from 0.0043 to 0.0433, $p<0.01$) (Wang et al., 2021).
• Benchmarking Against Baselines: End-to-end architectures consistently outperform pipeline-based and retrieval-based models across both text generation and image synthesis metrics. For instance, BLEU-1 and FID improvements hold for both DialogCC (BLEU-1: 8.08→15.84, FID: 108.50→59.63) and PhotoChat (Guo et al., 2024).
• Human and Adversarial Evaluation: Judges consistently report higher relevance, diversity, and readability for models leveraging mutual dependency and knowledge enrichment (Wang et al., 2021). User studies on multi-modal interactive generation show >70% preference for advanced systems in terms of both correct modality and coherence (Huang et al., 2024).

7. Challenges, Limitations, and Future Directions

• Data Sparsity and Grounding Drift: Real-world conversations often show declining visual grounding over turns; filtering and annotation strategies (e.g., MMChat-HF) are needed to obtain representative, dense contexts (Zheng et al., 2021).
• Explicit Grounding and Interest Point Detection: Dynamic attention over spatio-temporal segments and human-interests prediction remain unsolved, especially for richly multi-modal video-based chitchat (Lin et al., 2023).
• Scaling and Personalization: Challenges include personalized retrieval, hybrid symbolic–neuro reasoning, and bridging domain gaps via continual pretraining (Lin et al., 2023, Chen et al., 2023).
• Media Alignment and Evaluation: Ensuring object and attribute persistence across generated images and turns, as well as robustly measuring cross-modal response fidelity, are active research areas (Huang et al., 2024, Wang et al., 31 Jan 2025).
• Complex Synthesis: End-to-end audio–visual dialogue, zero-shot video dialogue crafting, and large-scale instruction-tuned multi-modal LLMs represent the current research frontier (Pang et al., 2 Dec 2025, Wang et al., 31 Jan 2025).

A plausible implication is that continual integration of larger, higher-quality datasets, advanced representation learning (e.g., Q-Former, CLIP, ViT–LLM pipelines), multi-level knowledge fusion, and evaluation standards will further accelerate progress towards open-domain, engaging, and semantically rich multi-modal dialogue agents that can operate in complex, dynamic sensory environments.