Large-scale visual-linguistic pre-training aims to capture the generic representations from multimodal features, which are essential for downstream vision-language tasks. Existing methods mostly focus on learning the seman- tic connections between visual objects and lin- guistic content, which tend to be recognition- level information and may not be sufficient for commonsensical reasoning tasks like VCR. In this paper, we propose a novel commonsen- sical vision-language pre-training framework to bridge the gap. We first augment the con- ventional image-caption pre-training datasets with commonsense inferences from a visual- linguistic GPT-2. To pre-train models on image, caption and commonsense inferences together, we propose two new tasks: masked common- sense modeling (MCM) and commonsense type prediction (CTP). To reduce the shortcut effect between captions and commonsense inferences, we further introduce the domain-wise adaptive masking that dynamically adjusts the mask- ing ratio. Experimental results on downstream tasks, VCR and VQA, show the improvement of our pre-training strategy over previous meth- ods. Human evaluation also validates the rel- evance, informativeness, and diversity of the generated commonsense inferences. Overall, we demonstrate the potential of incorporating commonsense knowledge into the conventional recognition-level visual-linguistic pre-training.

Bridging the Gap between Recognition-level Pre-training and Commonsensical Vision-language Tasks

**Organizers:** Antoine Bosselut, Xiang Lorraine Li, Bill Yuchen Lin, Vered Shwartz, Bodhisattwa Prasad Majumder, Yash Kumar Lal, Rachel Rudinger, Xiang Ren, Niket Tandon, Vilém Zouhar
 **Description:** We organize this workshop to encourage discussion of current progress on building machines with commonsense knowledge and reasoning abilities. We aim to bring together researchers from different areas (e.g., NLP, computer vision,
computational neuroscience, psychology) to communicate promising working directions in the area of commonsense
reasoning 
**Please visit our [website](https://csrr-workshop.github.io/)**

(CL_Commonsense)        Workshop on Commonsense Representation and Reasoning

workshop paper

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
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[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Yue Wan

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Bridging the Gap between Recognition-level Pre-training and Commonsensical Vision-language Tasks

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