Shivam Sharma


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DISARM: Detecting the Victims Targeted by Harmful Memes
Shivam Sharma | Md Shad Akhtar | Preslav Nakov | Tanmoy Chakraborty
Findings of the Association for Computational Linguistics: NAACL 2022

Internet memes have emerged as an increasingly popular means of communication on the web. Although memes are typically intended to elicit humour, they have been increasingly used to spread hatred, trolling, and cyberbullying, as well as to target specific individuals, communities, or society on political, socio-cultural, and psychological grounds. While previous work has focused on detecting harmful, hateful, and offensive memes in general, identifying whom these memes attack (i.e., the ‘victims’) remains a challenging and underexplored area. We attempt to address this problem in this paper. To this end, we create a dataset in which we annotate each meme with its victim(s) such as the name of the targeted person(s), organization(s), and community(ies). We then propose DISARM (Detecting vIctimS targeted by hARmful Memes), a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to, and then, incorporates a novel contextualized multimodal deep neural network to classify whether the meme intends to harm these entities. We perform several systematic experiments on three different test sets, corresponding to entities that are (i) all seen while training, (ii) not seen as a harmful target while training, and (iii) not seen at all while training. The evaluation shows that DISARM significantly outperforms 10 unimodal and multimodal systems. Finally, we demonstrate that DISARM is interpretable and comparatively more generalizable and that it can reduce the relative error rate of harmful target identification by up to 9 % absolute over multimodal baseline systems.

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Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes
Shivam Sharma | Tharun Suresh | Atharva Kulkarni | Himanshi Mathur | Preslav Nakov | Md. Shad Akhtar | Tanmoy Chakraborty
Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations

We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities. The task aims to delve deeper into the domain of meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanced terms, the shared task focuses on determining the victimizing, glorifying, and vilifying intentions embedded in meme entities to explicate their connotations. To this end, we curate HVVMemes, a novel meme dataset of about 7000 memes spanning the domains of COVID-19 and US Politics, each containing entities and their associated roles: hero, villain, victim, or none. The shared task attracted 105 participants, but eventually only 6 submissions were made. Most of the successful submissions relied on fine-tuning pre-trained language and multimodal models along with ensembles. The best submission achieved an F1-score of 58.67.

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Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned
Sameera Horawalavithana | Ellyn Ayton | Shivam Sharma | Scott Howland | Megha Subramanian | Scott Vasquez | Robin Cosbey | Maria Glenski | Svitlana Volkova
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e.g., law, healthcare, education, etc. However, only limited efforts have investigated the opportunities and limitations of applying these powerful models to science and security applications. In this work, we develop foundation models of scientific knowledge for chemistry to augment scientists with the advanced ability to perceive and reason at scale previously unimagined. Specifically, we build large-scale (1.47B parameter) general-purpose models for chemistry that can be effectively used to perform a wide range of in-domain and out-of-domain tasks. Evaluating these models in a zero-shot setting, we analyze the effect of model and data scaling, knowledge depth, and temporality on model performance in context of model training efficiency. Our novel findings demonstrate that (1) model size significantly contributes to the task performance when evaluated in a zero-shot setting; (2) data quality (aka diversity) affects model performance more than data quantity; (3) similarly, unlike previous work, temporal order of the documents in the corpus boosts model performance only for specific tasks, e.g., SciQ; and (4) models pre-trained from scratch perform better on in-domain tasks than those tuned from general-purpose models like Open AI’s GPT-2.

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Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis
Shivam Sharma | Mohd Khizir Siddiqui | Md. Shad Akhtar | Tanmoy Chakraborty
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Existing self-supervised learning strategies are constrained to either a limited set of objectives or generic downstream tasks that predominantly target uni-modal applications. This has isolated progress for imperative multi-modal applications that are diverse in terms of complexity and domain-affinity, such as meme analysis. Here, we introduce two self-supervised pre-training methods, namely Ext-PIE-Net and MM-SimCLR that (i) employ off-the-shelf multi-modal hate-speech data during pre-training and (ii) perform self-supervised learning by incorporating multiple specialized pretext tasks, effectively catering to the required complex multi-modal representation learning for meme analysis. We experiment with different self-supervision strategies, including potential variants that could help learn rich cross-modality representations and evaluate using popular linear probing on the Hateful Memes task. The proposed solutions strongly compete with the fully supervised baseline via label-efficient training while distinctly outperforming them on all three tasks of the Memotion challenge with 0.18%, 23.64%, and 0.93% performance gain, respectively. Further, we demonstrate the generalizability of the proposed solutions by reporting competitive performance on the HarMeme task. Finally, we empirically establish the quality of the learned representations by analyzing task-specific learning, using fewer labeled training samples, and arguing that the complexity of the self-supervision strategy and downstream task at hand are correlated. Our efforts highlight the requirement of better multi-modal self-supervision methods involving specialized pretext tasks for efficient fine-tuning and generalizable performance.


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Detecting Harmful Memes and Their Targets
Shraman Pramanick | Dimitar Dimitrov | Rituparna Mukherjee | Shivam Sharma | Md. Shad Akhtar | Preslav Nakov | Tanmoy Chakraborty
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets
Shraman Pramanick | Shivam Sharma | Dimitar Dimitrov | Md. Shad Akhtar | Preslav Nakov | Tanmoy Chakraborty
Findings of the Association for Computational Linguistics: EMNLP 2021

Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abuse. Detecting such memes is challenging as they can be highly satirical and cryptic. Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general. Here, we aim to bridge this gap. In particular, we focus on two tasks: (i)detecting harmful memes, and (ii) identifying the social entities they target. We further extend the recently released HarMeme dataset, which covered COVID-19, with additional memes and a new topic: US politics. To solve these tasks, we propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes. MOMENTA systematically analyzes the local and the global perspective of the input meme (in both modalities) and relates it to the background context. MOMENTA is interpretable and generalizable, and our experiments show that it outperforms several strong rivaling approaches.

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Evaluating and Explaining Natural Language Generation with GenX
Kayla Duskin | Shivam Sharma | Ji Young Yun | Emily Saldanha | Dustin Arendt
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

Current methods for evaluation of natural language generation models focus on measuring text quality but fail to probe the model creativity, i.e., its ability to generate novel but coherent text sequences not seen in the training corpus. We present the GenX tool which is designed to enable interactive exploration and explanation of natural language generation outputs with a focus on the detection of memorization. We demonstrate the utility of the tool on two domain-conditioned generation use cases - phishing emails and ACL abstracts.


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A Database of Infant Cry Sounds to Study the Likely Cause of Cry
Shivam Sharma | Shubham Asthana | V. K. Mittal
Proceedings of the 12th International Conference on Natural Language Processing