Bilingual Multimodal Dataset Insights

• Bilingual multimodal datasets are resources that systematically pair at least two languages with non-textual modalities, enabling tasks like visual reasoning and translation.
• They are constructed through rigorous methodologies including parallel alignment, quality-controlled annotation, and a mix of manual and automated curation.
• These datasets drive advances in cross-lingual pretraining and multitask learning, underpinning benchmarks in vision-language reasoning, dialogue, and content moderation.

Bilingual multimodal datasets comprise resources in which at least two human languages are systematically paired with multiple non-textual modalities such as images, video, audio, speech, or structured data. These datasets are foundational for the paper and development of models that can reason, translate, or generate across both language boundaries and sensory modalities. They support tasks including cross-lingual retrieval, multimodal machine translation, vision–language reasoning, dialogue, scientific question answering, and content moderation, among others. The landscape of bilingual multimodal datasets is diverse, spanning fundamental resources for contrastive learning, instructional dialogue, knowledge-intensive QA, meme reasoning, misinformation detection, scientific benchmarking, and more.

1. Dataset Scope and Types

Bilingual multimodal datasets vary broadly in construction, scale, and application domain, but share several structural features:

• Modalities: Typical combinations include text and images (Guo et al., 29 Jan 2024), video and text (Sanders et al., 2023), audio and text (Sikasote et al., 2023), images and speech (Sikasote et al., 2023), or composite sources such as charts, tables, and news (Xu et al., 5 Jun 2025).
• Language Pairs: English–Chinese, English–Korean, English–Bemba, English–German, Hindi–English, English–Portuguese, and English–Japanese are prevalent due to resource availability and research interest (Sikasote et al., 2023, Liang et al., 2022, Wang et al., 2021, Mukherjee et al., 12 Nov 2025).
• Data Structures: Datasets include strictly parallel resources (sentence/image pairs with direct translation), comparable data (independently created but thematically aligned pairs), conversation/dialogue, and instruction–response formats (Merritt et al., 2020, Sikasote et al., 2023).
• Domain Focus: Applications span open-domain visual reasoning (Wang et al., 24 Oct 2024, Guo et al., 29 Jan 2024), scientific and academic benchmarks (Xi et al., 4 Jul 2025, Mukherjee et al., 12 Nov 2025, He et al., 21 Feb 2024), financial forecasting (Xu et al., 5 Jun 2025), healthcare (Wang et al., 24 Oct 2024), harmful content detection (Gu et al., 15 Jun 2025), and unreliable news/media analysis (He et al., 28 Jun 2025).

2. Construction Methodologies and Alignment Protocols

Construction of bilingual multimodal datasets requires multimodal alignment and rigorous bilingual pairing:

• Parallelism: Strict text–text–modality triplets, e.g., (image, source language caption, translation), support supervised training and evaluation of cross-lingual multimodal models (Liang et al., 2022, Sikasote et al., 2023). Some efforts achieve high-quality alignment by manual translation and validation (e.g., all utterances in MSCTD (Liang et al., 2022) and BIG-C (Sikasote et al., 2023) are reviewed by expert annotators or advisors).
• Comparable Data: In scenarios where true translation and full parallelism are unavailable, comparable sentence pairs (distinct captions for the same image in two languages) are used (Merritt et al., 2020). While less aligned at the token level, these resources provide realistic, independently-authored views of the same semantic content.
• Automatic and Human Curation: Large-scale web mining, OCR, neural translation, and CLIP-based semantic filtering are standard for assembling billion-scale resources (Ko et al., 2022, Guo et al., 29 Jan 2024). High-quality datasets or benchmarks typically introduce multi-stage annotation, quality-control loops, and human verification (e.g., BMMR (Xi et al., 4 Jul 2025), MemeMind (Gu et al., 15 Jun 2025)), including systematic post-editing to resolve inconsistencies.
• Cross-modal and Cross-lingual Correspondence: The alignment is not restricted to text; image–text, video–text, and speech–text alignments are specifically engineered via timestamp synchronization, region-level annotations, or co-reference templates (e.g., region-bound biomedical VQA (Wang et al., 24 Oct 2024), MultiVENT (Sanders et al., 2023)).

3. Data Schema, Access, and Licensing

Bilingual multimodal datasets expose rich metadata and are structured for ease of programmatic access:

• File Formats: Standard formats include JSONL (per record), CSV/TSV (tabular annotations), and modality-specific storage for images (JPEG/PNG), audio (WAV), or video (MP4).
• Metadata Fields: Common schema elements are unique IDs, language codes, modality pointers (e.g., image IDs, audio filenames), and task-specific fields (e.g., bounding boxes, captions, sentiment, CoT).
• Licensing and Access: Many datasets are publicly available under Creative Commons or similar open/data-sharing licenses, with explicit provisions for research use (Sikasote et al., 2023, Guo et al., 29 Jan 2024, Liang et al., 2022). Some restrict commercial exploitation or withhold private splits for future benchmarking (Sikasote et al., 2023).
• Reproducibility: State-of-the-art datasets make data, code, and pre-trained models available in repositories, often with instructions for data retrieval (e.g., via GitHub or Zenodo), and provide pre-extracted features for direct use (Wang et al., 2021, Guo et al., 29 Jan 2024).

4. Supported Tasks and Benchmarking Protocols

Bilingual multimodal datasets are designed to support and benchmark a variety of research tasks:

Dataset	Tasks Supported	Modalities	Bilingual Pair
BIG-C (Sikasote et al., 2023)	ASR, ST, MT, dialogue QA	Audio, image, text	Bemba–English
BMMR (Xi et al., 4 Jul 2025)	Multidisciplinary reasoning, QA	Image, text, formulas	Chinese–English
MSCTD (Liang et al., 2022)	Chat MT, sentiment analysis	Image, text, sentiment	Chinese–English, German–English
MemeMind (Gu et al., 15 Jun 2025)	Harmful meme detection, CoT	Image, OCR/Text	Chinese–English
mmJEE-Eval (Mukherjee et al., 12 Nov 2025)	Scientific reasoning, STEM QA	Image, text, diagram	English–Hindi
PolyGlotFake (Hou et al., 14 May 2024)	Deepfake detection	Audio, video	7-languages

• Core Benchmarks: Speech recognition (WER), machine translation (BLEU), multimodal retrieval (Recall@k, Median Rank), VQA (accuracy), reasoning path consistency (stepwise scores), and explanation alignment (BERTScore) are standardized (Sikasote et al., 2023, Xi et al., 4 Jul 2025, Wang et al., 24 Oct 2024, He et al., 28 Jun 2025).
• Complex Tasks: Recent datasets increasingly offer chain-of-thought annotations (Gu et al., 15 Jun 2025), region-level localization (Wang et al., 24 Oct 2024, He et al., 28 Jun 2025), metacognitive reasoning protocols (Mukherjee et al., 12 Nov 2025), and context-rich dialogue or QA schemas spanning disciplines and modalities (Xi et al., 4 Jul 2025, He et al., 21 Feb 2024). Ground-truths include stepwise rationales, manipulated region masks, and bilingual QA pairs aligned at the utterance or entity level.

5. Impact, Limitations, and Applications

Bilingual multimodal datasets serve as both practical training sets and diagnostic benchmarks for emergent models:

• Advancing Multimodal Pretraining: Large resources such as BM-6B (Guo et al., 29 Jan 2024) underpin the development of bilingual vision–language foundation models, closing language gaps in zero-shot classification, retrieval, and captioning, especially for under-resourced languages (e.g., Chinese, Bemba).
• Cross-Disciplinary and Cultural Generalization: Datasets like BMMR (Xi et al., 4 Jul 2025) and mmJEE-Eval (Mukherjee et al., 12 Nov 2025) expose discipline and language biases, revealing the limitations of SOTA models outside the English-centric STEM data regime and enabling true cross-lingual scientific evaluation.
• Real-World Robustness and Explainability: Resources such as BiMiBench (He et al., 28 Jun 2025) and MemeMind (Gu et al., 15 Jun 2025) test models under sophisticated conditions of visual–textual manipulation, demand natural language explanations, and require joint cross-modal and cross-lingual reasoning in adversarial settings.
• Resource and Annotation Gaps: Limitations include domain bias (e.g., subtitles, news), uneven distribution of harm categories or part-of-speech coverage, image-only grounding (no bounding boxes), and the challenge of scaling precise bilingual region/phrase alignment (Wang et al., 2021, Sanders et al., 2023).
• Best Practices: Stratified batch sampling, language-balanced corpora, context-aware annotation, and modular architectures are standard to ensure coverage and minimize overfitting (Guo et al., 29 Jan 2024, He et al., 28 Jun 2025). Extending coverage to low-resource languages, expanding to new modalities, and advancing fine-grained alignment are ongoing priorities.

6. Future Directions

Open challenges and emerging directions highlighted in the literature include:

• Expanding Language and Domain Coverage: Extension to additional low-resource or typologically diverse language pairs (e.g., Thai, Vietnamese, Hebrew), domain-specific datasets (e.g., medical, financial, legal), and more complex data modalities (temporal, tabular, geospatial) (Xu et al., 5 Jun 2025, Wang et al., 24 Oct 2024).
• Fine-Grained Grounding: Scaling reliable region-level, entity-phrase, or event-segment alignment—currently rare outside specialized collections—remains critical for robust, compositional grounding (Wang et al., 24 Oct 2024).
• Ethical and Societal Considerations: As forecast models and misinformation detectors gain effectiveness, new datasets and benchmarks are needed for ethical alignment, fairness, and language bias mitigation (Xu et al., 5 Jun 2025, He et al., 28 Jun 2025).
• Multitask and Multidisciplinary Modeling: Construction of datasets emphasizing joint multitask learning—enabling models to generalize across translation, reasoning, retrieval, and summarization in both bilingual and multimodal settings—is an active area (Armitage et al., 2020, Xi et al., 4 Jul 2025).

Bilingual multimodal datasets are now a cornerstone for evaluating and training the next generation of language, vision, and speech models that operate robustly across linguistic, cultural, and perceptual divides, with community-driven resources rapidly advancing the breadth and depth of this ecosystem.