Papers by Yash Goyal
Order-Based Pre-training Strategies for Procedural Text Understanding (2024.naacl-short)
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| Challenge: | Procedural text is difficult to understand due to the changing attributes of entities in the context. |
| Approach: | They propose sequence-based pre-training methods to enhance procedural understanding in natural language processing by using ordered instructions to guide individuals through a task. |
| Outcome: | The proposed methods improve on two datasets in the datasets NPN-Cooking and ProPara domains respectively. |
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting (2023.eacl-main)
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| Challenge: | Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. |
| Approach: | They propose to use frozen unimodal models to learn a lightweight mapping between the representation spaces of unimod models using aligned image-text data. |
| Outcome: | The proposed method can generalize to unseen VL tasks from a few in-context examples while training orders of magnitude fewer parameters. |
CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics (2025.findings-emnlp)
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Shravan Nayak, Mehar Bhatia, Xiaofeng Zhang, Verena Rieser, Lisa Anne Hendricks, Sjoerd Van Steenkiste, Yash Goyal, Karolina Stanczak, Aishwarya Agrawal
| Challenge: | CulturalFrames is a benchmark designed for rigorous human evaluation of cultural representation in visual generations. |
| Approach: | They propose to quantify the alignment of T2I models and evaluation metrics with respect to both explicit (stated) and implicit (unstated, implied by the prompt’s cultural context) cultural expectations. |
| Outcome: | The proposed model is based on 983 prompts, 3637 images and 10k human annotations from 10 countries and 5 socio-cultural domains. |
***YesBut***: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models (2024.emnlp-main)
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Abhilash Nandy, Yash Agarwal, Ashish Patwa, Millon Das, Aman Bansal, Ankit Raj, Pawan Goyal, Niloy Ganguly
| Challenge: | Existing Vision-Language models perform poorly on satirical image detecting tasks . satire and humor are powerful tools to highlight issues, provoke thought, and encourage critical perspective . |
| Approach: | They propose to use a dataset to evaluate satirical images and satire images to detect satiric images . they also propose to generate the reason behind the image being satiral by generating one half of the image to be satisfying . |
| Outcome: | The proposed dataset contains 2547 images, 1084 satirical and 1463 non-satirically, with different artistic styles. |
Promoting Topic Coherence and Inter-Document Consorts in Multi-Document Summarization via Simplicial Complex and Sheaf Graph (2023.emnlp-main)
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| Challenge: | Existing systems that generate summaries from multiple sources often lack accuracy and accuracy due to the length of tokens used in encoding. |
| Approach: | They propose a novel encoder-decoder model that uses pre-trained BART to analyze linguistic nuances, simplicial complex layer to apprehend inherent properties that transcend pairwise associations and sheaf graph attention to effectively capture heterophilic properties. |
| Outcome: | The proposed model achieves consistent performance improvement across all evaluation metrics (syntactical, semantical and faithfulness). |
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for tagging opinion triplets fail to capture the strong interdependence between the three opinion factors, whereas grid tabbing fails to capture span-level semantics while predicting sentiment between an aspect-opinion pair. |
| Approach: | They propose a tagging-free approach to extracting opinion triplets using a pointer network decoding framework that captures the interdependence between the three elements of an opinion triple. |
| Outcome: | The proposed architecture captures the interdependence between the aspect and opinion triplets while predicting their connecting sentiment. |
Image Retrieval from Contextual Descriptions (2022.acl-long)
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| Challenge: | a new multimodal challenge challenges vision-and-language models to integrate context into their representations. |
| Approach: | They propose a multimodal challenge to integrate context into vision-and-language models . they benchmark several state-of-the-art models using cross-encoders and bi-encodings . |
| Outcome: | The proposed model lags behind human models on imageCoDe, compared with human models. |