Visual Goal-Step Inference using wikiHow

Yue Yang, Artemis Panagopoulou, Qing Lyu, Li Zhang, Mark Yatskar, Chris Callison-Burch


Abstract
Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities. Past work in NLP has examined the task of goal-step inference for text. We introduce the visual analogue. We propose the Visual Goal-Step Inference (VGSI) task, where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. With a new dataset harvested from wikiHow consisting of 772,277 images representing human actions, we show that our task is challenging for state-of-the-art multimodal models. Moreover, the multimodal representation learned from our data can be effectively transferred to other datasets like HowTo100m, increasing the VGSI accuracy by 15 - 20%. Our task will facilitate multimodal reasoning about procedural events.
Anthology ID:
2021.emnlp-main.165
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2167–2179
Language:
URL:
https://aclanthology.org/2021.emnlp-main.165
DOI:
10.18653/v1/2021.emnlp-main.165
Bibkey:
Cite (ACL):
Yue Yang, Artemis Panagopoulou, Qing Lyu, Li Zhang, Mark Yatskar, and Chris Callison-Burch. 2021. Visual Goal-Step Inference using wikiHow. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2167–2179, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Visual Goal-Step Inference using wikiHow (Yang et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.165.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.165.mp4
Code
 yueyang1996/wikihow-vgsi
Data
wikiHow-imageCOINHowTo100M