Gargi Ghosh


Multi-Task Retrieval for Knowledge-Intensive Tasks
Jean Maillard | Vladimir Karpukhin | Fabio Petroni | Wen-tau Yih | Barlas Oguz | Veselin Stoyanov | Gargi Ghosh
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of whether a neural retrieval model can be _universal_ and perform robustly on a wide variety of problems, we propose a multi-task trained model. Our approach not only outperforms previous methods in the few-shot setting, but also rivals specialised neural retrievers, even when in-domain training data is abundant. With the help of our retriever, we improve existing models for downstream tasks and closely match or improve the state of the art on multiple benchmarks.

VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding
Hu Xu | Gargi Ghosh | Po-Yao Huang | Prahal Arora | Masoumeh Aminzadeh | Christoph Feichtenhofer | Florian Metze | Luke Zettlemoyer
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
Hu Xu | Gargi Ghosh | Po-Yao Huang | Dmytro Okhonko | Armen Aghajanyan | Florian Metze | Luke Zettlemoyer | Christoph Feichtenhofer
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks. VideoCLIP trains a transformer for video and text by contrasting temporally overlapping positive video-text pairs with hard negatives from nearest neighbor retrieval. Our experiments on a diverse series of downstream tasks, including sequence-level text-video retrieval, VideoQA, token-level action localization, and action segmentation reveal state-of-the-art performance, surpassing prior work, and in some cases even outperforming supervised approaches. Code is made available at


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Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Fan Yang | Xiaochang Peng | Gargi Ghosh | Reshef Shilon | Hao Ma | Eider Moore | Goran Predovic
Proceedings of the Third Workshop on Abusive Language Online

Interactions among users on social network platforms are usually positive, constructive and insightful. However, sometimes people also get exposed to objectionable content such as hate speech, bullying, and verbal abuse etc. Most social platforms have explicit policy against hate speech because it creates an environment of intimidation and exclusion, and in some cases may promote real-world violence. As users’ interactions on today’s social networks involve multiple modalities, such as texts, images and videos, in this paper we explore the challenge of automatically identifying hate speech with deep multimodal technologies, extending previous research which mostly focuses on the text signal alone. We present a number of fusion approaches to integrate text and photo signals. We show that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.