Challenge: a multimodal multilingual lecture companion is needed to preserve lecture content in its entirety . globalization of education and rapid growth of online learning have made localizing educational content a challenge .
Approach: They propose a multimodal multilingual lecture companion that translates lecture audio and slides to produce synchronized outputs across three modalities.
Outcome: The proposed solution preserves the original content in its entirety while preserving translations across three modalities.

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KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning (C18-2)

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Challenge: In today's globalized world, communication is difficult and often the language barrier still prevents communication.
Approach: They have developed a low-latency translation system that is adapted to lectures and covers several language pairs.
Outcome: The proposed system improves performance but also covers several European languages.
UniSpeaker: A Unified Approach for Multimodality-driven Speaker Generation (2025.findings-emnlp)

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Challenge: a new framework for speaker generation is proposed to enable multimodal speaker generation . multimodal cues such as visual appearance, textual descriptions, and other biometric signals are still in its early stages.
Approach: a new framework is proposed to enable multimodal speaker generation . the framework uses self-distillation to apply speaker disentanglement to speech generation a model is developed .
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Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion (2026.acl-long)

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Challenge: Existing methods for audio captioning lack fine-grained detail and contextual accuracy due to limited unimodal or superficial information.
Approach: They propose a two-stage automated pipeline that uses pretrained models to extract contextual cues from video . a large language model synthesizes these inputs to generate detailed and context-aware captions .
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Multimodal large language models for inclusive collaboration learning tasks (2022.naacl-srw)

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Challenge: This project leverages advances in multimodal large language models to build an inclusive collaboration feedback loop for participants developing general collaboration skills.
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Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)

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Challenge: Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved.
Approach: They propose an Adaptive Language-guided Multimodal Transformer (ALMT) that learns an irrelevance/conflict-suppressing representation from visual and audio features under guidance of language features at different scales.
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M3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset (2024.acl-long)

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Challenge: Publishing open-source academic video recordings is an emerging approach to sharing knowledge online.
Approach: They propose a multimodal, multigenre, and multipurpose audio-visual academic lecture dataset with human annotations for multimodal content recognition and understanding tasks.
Outcome: The proposed dataset can be used for multiple audio-visual recognition and understanding tasks.
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities (2023.findings-emnlp)

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Challenge: Existing multi-modal large language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer.
Approach: They propose a large language model with intrinsic cross-modal conversational abilities . they construct a cross-text speech instruction dataset and employ a three-stage training strategy .
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Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation (2023.findings-acl)

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Challenge: Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision.
Approach: They propose a framework for multimodal machine translation that utilizes large-scale non-triple data and a multimodal translation dataset.
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Enhancing Large Language Models for Scientific Multimodal Summarization with Multimodal Output (2025.coling-industry)

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Challenge: Scientific publications are becoming more multimedia, containing both text and visual content.
Approach: They propose a framework for Scientific Multimodal Summarization with Multimodal Output . it leverages the power of large language models and extends its capability to cross-modal understanding .
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Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos (2022.emnlp-main)

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Challenge: Existing multimodal summarization methods are limited to monolingual videos . a proposed task aims to generate cross-lingual summaries from multimodal inputs .
Approach: They propose a task to generate cross-lingual summaries from multimodal inputs of videos . they propose fusion network that integrates multimodal and cross-linguistic information .
Outcome: The proposed task outperforms existing methods on a reorganized How2 dataset on the reorganized How2 data set.

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