Figurative expressions, e.g., ``black sheep," ``piece of cake," and ``rocket science," pose challenges for language models, which struggle to grasp their semantic meanings and the nuanced context in which they are used. 
or
..., aiming to assess semantic understanding across modalities
A few sentences summarizing the paper.
This paper describes CAMR, the transition- based parser that we use in the SemEval-2016 Meaning Representation Parsing task.
This paper describes our participation in SemEval-2024 Task 1, a multimodal idiomaticity representation. In this task, we have to ...
The ADMIRE Subtask A - Static Images aims to tackle ..., and it inherits the ... of the ADMIRE I task. The previous task aimed ... However, they still suffer from ....
In this paper, our team ALVJ-Transformer\footnote{This work was done for project work for Trnsformer seminar.} proposes a ... system (System name) for ... We perform analysis on the previous top systems and reveal that their performance bottleneck lies in ... .
Also, we discover that...
To enhance the retrieval context, we incorporate the MagPie knowledge base to broaden the contextual scope of the model. 
Also, we explore various search strategies.
Our system1 wins ... out of ... tracks in the ADMIRE Subtask A. Additionally, we compared our system with ChatGPT, one of the large language models which have unlocked strong capabilities on many tasks. The results show that there is still much room for improvement for ChatGPT on the extraction task.

We participated in the SemEval 2024 Task 1 \citep{picard} Subtask A, Static Images: where participants were presented with a set of 5 images and a context sentence in which a particular potentially idiomatic nominal compound (NC) appears. The goal was to rank the images according to how well they represent the sense in which the NC is used in the given context sentence, assessing performance through Top Image Accuracy (correct identification of the most representative caption) and Rank Correlation(Spearman’s rank correlation of model rankings with ground truth).
Each NC could be interpreted as either idiomatic or literal. Two settings were available for the subtask; one in which only the text is available, and one which uses the images. We used a variation of the task in which the images were replaced with text captions describing their content. 
The task provided two data sets English and Portuguese. We focused on English. 

Using the Magpie corpus and ChatGPT-generated descriptions, this approach aims to advance multimodal idiomatic understanding. Our outcome has been proven positive, showing that our model effectively enhances the alignment between figurative language and its visual representations.

Our system utilizes BERT-based model (sentence embeddings to map idiomatic expressions to text captions and rank them using cosine similarity)
We include an error analysis with visualizations of feature contributions, illustrating how our system advances the state-of-the-art in multimodal idiomaticity representation.

Performance was measured using Top Image Accuracy (correctly identifying the most representative image) and Rank Correlation (Spearman’s rank correlation between the model's rankings and the ground truth).