Papers Multimodal Machine Translation
“Multimodal Machine Translation” 태그가 달린 논문 117편 · 필터 해제
ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation
We present ForMaT (Format-Preserving Multilingual Translation), a parallel corpus of 3,956 PDFs across 15 language pairs that preserves original layout metadata proposed for multimodal machine translation. To ensure stru…
Multimodal Machine TranslationVIDA: A dataset for Visually Dependent Ambiguity in Multimodal Machine Translation
Ambiguity resolution is a key challenge in multimodal machine translation (MMT), where models must genuinely leverage visual input to map an ambiguous expression to its intended meaning. Although prior work has proposed …
Multimodal Machine TranslationScalable Multilingual Multimodal Machine Translation with Speech-Text Fusion
Multimodal Large Language Models (MLLMs) have achieved notable success in enhancing translation performance by integrating multimodal information. However, existing research primarily focuses on image-guided methods, who…
Multimodal Machine TranslationMultiScript30k: Leveraging Multilingual Embeddings to Extend Cross Script Parallel Data
Multi30k is frequently cited in the multimodal machine translation (MMT) literature, offering parallel text data for training and fine-tuning deep learning models. However, it is limited to four languages: Czech, English…
Multimodal Machine TranslationA Picture is Worth a Thousand (Correct) Captions: A Vision-Guided Judge-Corrector System for Multimodal Machine Translation
In this paper, we describe our system under the team name BLEU Monday for the English-to-Indic Multimodal Translation Task at WAT 2025. We participate in the text-only translation tasks for English-Hindi, English-Bengali…
Multimodal Machine TranslationIndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs
Vision-language models (VLMs) have demonstrated impressive generalization across multimodal tasks, yet most evaluation benchmarks remain Western-centric, leaving open questions about their performance in culturally diver…
Multimodal Machine TranslationVisual Question AnsweringLLaVA-NeuMT: Selective Layer-Neuron Modulation for Efficient Multilingual Multimodal Translation
Multimodal Machine Translation (MMT) enhances translation quality by incorporating visual context, helping to resolve textual ambiguities. While existing MMT methods perform well in bilingual settings, extending them to …
Multimodal Machine TranslationGIIFT: Graph-guided Inductive Image-free Multimodal Machine Translation
Multimodal Machine Translation (MMT) has demonstrated the significant help of visual information in machine translation. However, existing MMT methods face challenges in leveraging the modality gap by enforcing rigid vis…
Multimodal Machine TranslationDual-branch Prompting for Multimodal Machine Translation
Multimodal Machine Translation (MMT) typically enhances text-only translation by incorporating aligned visual features. Despite the remarkable progress, state-of-the-art MMT approaches often rely on paired image-text inp…
Multimodal Machine TranslationCaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
Cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meani…
BenchmarkingMachine TranslationMultimodal Machine TranslationTranslationMultimodal Machine Translation with Visual Scene Graph Pruning
Multimodal machine translation (MMT) seeks to address the challenges posed by linguistic polysemy and ambiguity in translation tasks by incorporating visual information. A key bottleneck in current MMT research is the ef…
Machine TranslationMultimodal Machine TranslationTranslationTopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries
Most existing multimodal machine translation (MMT) datasets are predominantly composed of static images or short video clips, lacking extensive video data across diverse domains and topics. As a result, they fail to meet…
Domain AdaptationMachine TranslationMultimodal Machine TranslationNMT+1Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models
Cross-lingual transfer enables vision-language models (VLMs) to perform vision tasks in various languages with training data only in one language. Current approaches rely on large pre-trained multilingual language models…
Cross-Lingual TransferImage CaptioningLarge Language ModelMachine Translation+3Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation
Visual information has been introduced for enhancing machine translation (MT), and its effectiveness heavily relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations.…
Language ModelingLanguage ModellingLarge Language ModelMachine Translation+3EMMeTT: Efficient Multimodal Machine Translation Training
A rising interest in the modality extension of foundation language models warrants discussion on the most effective, and efficient, multimodal training approach. This work focuses on neural machine translation (NMT) and …
automatic-speech-translationDecoderMachine TranslationMultimodal Machine Translation+2Towards Zero-Shot Multimodal Machine Translation
Current multimodal machine translation (MMT) systems rely on fully supervised data (i.e models are trained on sentences with their translations and accompanying images). However, this type of data is costly to collect, l…
Language ModellingMachine TranslationMultimodal Machine TranslationTranslation3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
Multimodal machine translation (MMT) is a challenging task that seeks to improve translation quality by incorporating visual information. However, recent studies have indicated that the visual information provided by exi…
Machine TranslationMultimodal Machine TranslationSentenceTranslation+1Exploring the Necessity of Visual Modality in Multimodal Machine Translation using Authentic Datasets
Recent research in the field of multimodal machine translation (MMT) has indicated that the visual modality is either dispensable or offers only marginal advantages. However, most of these conclusions are drawn from the …
Machine TranslationMultimodal Machine TranslationSentenceTranslationDetecting Concrete Visual Tokens for Multimodal Machine Translation
The challenge of visual grounding and masking in multimodal machine translation (MMT) systems has encouraged varying approaches to the detection and selection of visually-grounded text tokens for masking. We introduce ne…
Machine TranslationMultimodal Machine Translationobject-detectionObject Detection+2Adding Multimodal Capabilities to a Text-only Translation Model
While most current work in multimodal machine translation (MMT) uses the Multi30k dataset for training and evaluation, we find that the resulting models overfit to the Multi30k dataset to an extreme degree. Consequently,…
Machine TranslationMultimodal Machine TranslationTranslation