Neural language models encode rich knowledge about entities and their relationships which can be extracted from their representations using probing. Common properties of nouns (e.g., red strawberries, small ant) are, however, more challenging to extract compared to other types of knowledge because they are rarely explicitly stated in texts. We hypothesize this to mainly be the case for perceptual properties which are obvious to the participants in the communication. We propose to extract these properties from images and use them in an ensemble model, in order to complement the information that is extracted from language models. We consider perceptual properties to be more concrete than abstract properties (e.g., interesting, flawless). We propose to use the adjectives' concreteness score as a lever to calibrate the contribution of each source (text vs. images). We evaluate our ensemble model in a ranking task where the actual properties of a noun need to be ranked higher than other non-relevant properties. Our results show that the proposed combination of text and images greatly improves noun property prediction compared to powerful text-based language models.

Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction

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.

Visual Goal-Step Inference using wikiHow

Neural language models encode rich knowledge about entities and their relationships which can be extracted from their representations using probing. Common properties of nouns (e.g., red strawberries, small ant) are, however, more challenging to extract compared to other types of knowledge because they are rarely explicitly stated in texts.
We hypothesize this to mainly be the case for perceptual properties which are obvious to the participants in the communication. We propose to extract these properties from images and use them in an ensemble model, in order to complement the information that is extracted from language models. We consider perceptual properties to be more concrete than abstract properties (e.g., interesting, flawless). We propose to use the adjectives' concreteness score as a lever to calibrate the contribution of each source (text vs. images). We evaluate our ensemble model in a ranking task where the actual properties of a noun need to be ranked higher than other non-relevant properties. Our results show that the proposed combination of text and images greatly improves noun property prediction compared to powerful text-based language models.

**Chair:** Debanjan Ghosh (remote) **Organizers:** Debanjan Ghosh, Beata Beigman Klebanov, Smaranda Muresan, Anna Feldman, Soujanya Poria and Tuhin Chakrabarty Please visit our [**website**](https://sites.google.com/view/figlang2022) for more information and detailed schedule.

**RocketChat channel for this workshop:** https://emnlp2022.rocket.chat/channel/workshop-17-flp

W17 (FLP) 3rd Workshop on Figurative Language Processing

workshop paper

## Welcome to EMNLP 2022!
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4F: Speech, Vision, Robotics, Multimodal Grounding 1

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Artemis Panagopoulou

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Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction

Visual Goal-Step Inference using wikiHow

Visual Goal-Step Inference using wikiHow

Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction

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