Papers by Krishna Garg
Data Augmentation for Low-Resource Keyphrase Generation (2023.findings-acl)
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| Challenge: | Existing works on keyphrase generation rely on large-scale annotated datasets, which are not easy to acquire. |
| Approach: | They propose to use full text to improve keyphrase generation in resource-constrained domains by using the full text of the articles to augment their methods. |
| Outcome: | The proposed methods improve both present and absent keyphrase generation on three datasets and show that they are cost-effective. |
Keyphrase Generation Beyond the Boundaries of Title and Abstract (2022.findings-emnlp)
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| Challenge: | Current approaches to keyphrase generation use only the title and abstract of the articles. |
| Approach: | They propose to integrate full text and semantically similar articles to generate keyphrases from a dataset that includes the full text of the articles along with the title and abstract. |
| Outcome: | The proposed model can generate keyphrases that are present or absent from the text. |
Stanceformer: Target-Aware Transformer for Stance Detection (2024.findings-emnlp)
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| Challenge: | Existing transformer models that lack the capability to prioritize targets under-perform and are underperforming the task. |
| Approach: | They propose a target-aware transformer model that incorporates enhanced attention towards the targets during both training and inference. |
| Outcome: | The proposed model improves on state-of-the-art models and Large Language Models and can be used for other domains. |
A New Direction in Stance Detection: Target-Stance Extraction in the Wild (2023.acl-long)
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| Challenge: | Existing methods for stance detection assume that the target is known in advance . Existing tasks use implicit mentions in the source text and are infeasible to have manual annotations at a large scale. |
| Approach: | They propose a task Target-Stance Extraction that aims to extract the (target, stance) pair from social media texts. |
| Outcome: | The proposed task can facilitate future research in the field of stance detection. |
ImageInWords: Unlocking Hyper-Detailed Image Descriptions (2024.emnlp-main)
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Roopal Garg, Andrea Burns, Burcu Karagol Ayan, Yonatan Bitton, Ceslee Montgomery, Yasumasa Onoe, Andrew Bunner, Ranjay Krishna, Jason Baldridge, Radu Soricut
| Challenge: | generating accurate hyper-detailed image descriptions is challenging for vision-language models trained on web-scraped image-text. |
| Approach: | They propose a data-centric framework for generating hyper-detailed image descriptions using web-scraped image-text. |
| Outcome: | The proposed framework improves on human evaluations on the data, even with only 9k samples. |
Downstream Datasets Make Surprisingly Good Pretraining Corpora (2023.acl-long)
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| Challenge: | a dominant practice is to fine tune large pretrained transformer models using smaller downstream datasets . performance gains are not always attributable to the use of external data in massive amounts . |
| Approach: | They propose to use the same (downstream) training data for pretraining and finetuning to compare models. |
| Outcome: | The proposed model outperforms standard pretraining on the BookWiki corpus on 7 and 5 datasets. |