@inproceedings{imrattanatrai-etal-2026-vdact,
title = "{VDA}ct 2.0: Scaling Video-Grounded Dialogue for Event-driven Activity Understanding with {LLM}-Assisted Filtering",
author = "Imrattanatrai, Wiradee and
Asada, Masaki and
Hasegawa, Kimihiro and
Fukuda, Ken and
Mitamura, Teruko",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.208/",
doi = "10.63317/4vcv4ncvs6xx",
pages = "2653--2666",
abstract = "We present VDAct 2.0, an enhanced benchmark for video-grounded dialogue that builds upon the original VDAct by expanding dialogue coverage and introducing a scalable LLM-assisted filtering pipeline to ensure high-quality, grounded QA pairs. VDAct 2.0 comprises 6,356 human-annotated dialogues with a total of 63,958 turns, grounded in 2,975 household activity videos, with undesirable dialogue turns systematically identified and removed. To achieve this, we design a trigger-based quality framework and calibrate a panel of high-agreement LLMs through human-in-the-loop calibration, allowing scalable QA-turn-level filtering. We benchmark a wide range of pretrained and fine-tuned models, both open-source and proprietary, across standard text generation metrics and LLM-based evaluations. The results highlight both recent advances and remaining challenges in video-grounded dialogue modeling, positioning VDAct 2.0 as a high-fidelity testbed for evaluating and advancing multimodal reasoning in interactive settings."
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<abstract>We present VDAct 2.0, an enhanced benchmark for video-grounded dialogue that builds upon the original VDAct by expanding dialogue coverage and introducing a scalable LLM-assisted filtering pipeline to ensure high-quality, grounded QA pairs. VDAct 2.0 comprises 6,356 human-annotated dialogues with a total of 63,958 turns, grounded in 2,975 household activity videos, with undesirable dialogue turns systematically identified and removed. To achieve this, we design a trigger-based quality framework and calibrate a panel of high-agreement LLMs through human-in-the-loop calibration, allowing scalable QA-turn-level filtering. We benchmark a wide range of pretrained and fine-tuned models, both open-source and proprietary, across standard text generation metrics and LLM-based evaluations. The results highlight both recent advances and remaining challenges in video-grounded dialogue modeling, positioning VDAct 2.0 as a high-fidelity testbed for evaluating and advancing multimodal reasoning in interactive settings.</abstract>
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%0 Conference Proceedings
%T VDAct 2.0: Scaling Video-Grounded Dialogue for Event-driven Activity Understanding with LLM-Assisted Filtering
%A Imrattanatrai, Wiradee
%A Asada, Masaki
%A Hasegawa, Kimihiro
%A Fukuda, Ken
%A Mitamura, Teruko
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F imrattanatrai-etal-2026-vdact
%X We present VDAct 2.0, an enhanced benchmark for video-grounded dialogue that builds upon the original VDAct by expanding dialogue coverage and introducing a scalable LLM-assisted filtering pipeline to ensure high-quality, grounded QA pairs. VDAct 2.0 comprises 6,356 human-annotated dialogues with a total of 63,958 turns, grounded in 2,975 household activity videos, with undesirable dialogue turns systematically identified and removed. To achieve this, we design a trigger-based quality framework and calibrate a panel of high-agreement LLMs through human-in-the-loop calibration, allowing scalable QA-turn-level filtering. We benchmark a wide range of pretrained and fine-tuned models, both open-source and proprietary, across standard text generation metrics and LLM-based evaluations. The results highlight both recent advances and remaining challenges in video-grounded dialogue modeling, positioning VDAct 2.0 as a high-fidelity testbed for evaluating and advancing multimodal reasoning in interactive settings.
%R 10.63317/4vcv4ncvs6xx
%U https://aclanthology.org/2026.lrec-1.208/
%U https://doi.org/10.63317/4vcv4ncvs6xx
%P 2653-2666
Markdown (Informal)
[VDAct 2.0: Scaling Video-Grounded Dialogue for Event-driven Activity Understanding with LLM-Assisted Filtering](https://aclanthology.org/2026.lrec-1.208/) (Imrattanatrai et al., LREC 2026)
ACL