publications

2023

1. ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER

   Sreyan Ghosh, Utkarsh Tyagi, Manan Suri, Sonal Kumar, Ramaneswaran S, and Dinesh Manocha

   In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2023

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   Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keyword selection for Conditional Language Model fine-tuning), a novel data augmentation approach based on conditional generation, to address the data scarcity problem in low-resource complex NER. ACLM alleviates the context-entity mismatch issue, a problem existing NER data augmentation techniques suffer from and often generates incoherent augmentations by placing complex named entities in the wrong context. ACLM builds on BART and is optimized on a novel text reconstruction or denoising task - we use selective masking (aided by attention maps) to retain the named entities and certain keywords in the input sentence that provide contextually relevant additional knowledge or hints about the named entities. Compared with other data augmentation strategies, ACLM can generate more diverse and coherent augmentations preserving the true word sense of complex entities in the sentence. We demonstrate the effectiveness of ACLM both qualitatively and quantitatively on monolingual, cross-lingual, and multilingual complex NER across various low-resource settings. ACLM outperforms all our neural baselines by a significant margin (1%-36%). In addition, we demonstrate the application of ACLM to other domains that suffer from data scarcity (e.g., biomedical). In practice, ACLM generates more effective and factual augmentations for these domains than prior methods.

   @inproceedings{ghosh-etal-2023-aclm,
     title = {{ACLM}: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex {NER}},
     author = {Ghosh, Sreyan and Tyagi, Utkarsh and Suri, Manan and Kumar, Sonal and S, Ramaneswaran and Manocha, Dinesh},
     booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
     month = jul,
     year = {2023},
     address = {Toronto, Canada},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.acl-long.8},
     doi = {10.18653/v1/2023.acl-long.8},
     pages = {104--125},
   }

2. The Geometry of Multilingual Language Models: An Equality Lens

   Cheril Shah, Yashashree Chandak, and Manan Suri

   In The First Tiny Papers Track at ICLR 2023, Tiny Papers @ ICLR 2023, Kigali, Rwanda, May 5, 2023, Jul 2023
3. CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network

   Sreyan Ghosh, Manan Suri, Purva Chiniya, Utkarsh Tyagi, Sonal Kumar, and Dinesh Manocha

   In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Dec 2023

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   The tremendous growth of social media users interacting in online conversations has led to significant growth in hate speech affecting people from various demographics. Most of the prior works focus on detecting explicit hate speech, which is overt and leverages hateful phrases, with very little work focusing on detecting hate speech that is implicit or denotes hatred through indirect or coded language. In this paper, we present CoSyn, a context synergized neural network that explicitly incorporates user- and conversational-context for detecting implicit hate speech in online conversations. CoSyn introduces novel ways to encode these external contexts and employs a novel context interaction mechanism that clearly captures the interplay between them, making independent assessments of the amounts of information to be retrieved from these noisy contexts. Additionally, it carries out all these operations in the hyperbolic space to account for the scale-free dynamics of social media. We demonstrate the effectiveness of CoSyn on 6 hate speech datasets and show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%. We make our code available.

4. WADER: A Weak-labelling framework for Data augmentation in tExt Regression Tasks

   Manan Suri, Aaryak Garg, Divya Chaudhary, Ian Gorton, and Bijendra Kumar

   In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), Jul 2023

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   Intimacy is an essential element of human relationships and language is a crucial means of conveying it. Textual intimacy analysis can reveal social norms in different contexts and serve as a benchmark for testing computational models’ ability to understand social information. In this paper, we propose a novel weak-labeling strategy for data augmentation in text regression tasks called WADER. WADER uses data augmentation to address the problems of data imbalance and data scarcity and provides a method for data augmentation in cross-lingual, zero-shot tasks. We benchmark the performance of State-of-the-Art pre-trained multilingual language models using WADER and analyze the use of sampling techniques to mitigate bias in data and optimally select augmentation candidates. Our results show that WADER outperforms the baseline model and provides a direction for mitigating data imbalance and scarcity in text regression tasks.

5. Multimodal Analysis and Modality Fusion for Detection of Depression from Twitter Data

   Nalin Semwal, Manan Suri, Divya Chaudhary, Ian Gorton, and Bijendra Kumar

   AI4SG at AAAI 2023: Workshop on Artificial Intelligence for Social Good, Association for the Advancement of Artificial Intelligence, Jul 2023
6. I Don’t Feel so Good! Detecting Depressive Tendencies Using Transformer-Based Multimodal Frameworks

   Manan Suri, Nalin Semwal, Divya Chaudhary, Ian Gorton, and Bijendra Kumar

   In Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing, Jul 2023

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   One of the most common mental illnesses that affects 5% of adults globally is depression. The advancement of social media has meant that more and more people have gained a platform to voice their thoughts and beliefs. People’s social media interactions and posted content can be used to infer critical characteristics such as depressive tendencies which will allow for timely intervention and help. This paper describes a novel supervised approach to detect depressive tendencies in Twitter users using multimodal frameworks which account for user interaction and online behaviour in addition to the tweet content processed using transformers like BERT. The performance of three multimodal frameworks is described with different methods for combining modalities. The best result is obtained a cross-modality based model which improves the baseline by 12% points.

2022

1. Multilingual Protest Event Detection using Transformer-based Models

   Manan Suri, Krish Chopra, and Adwita Arora

   In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), Dec 2022

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   Event detection, specifically in the socio-political domain, has posed a long-standing challenge to researchers in the NLP domain. Therefore, the creation of automated techniques that perform classification of the large amounts of accessible data on the Internet becomes imperative. This paper is a summary of the efforts we made in participating in Task 1 of CASE 2022. We use state-of-art multilingual BERT (mBERT) with further fine-tuning to perform document classification in English, Portuguese, Spanish, Urdu, Hindi, Turkish and Mandarin. In the document classification subtask, we were able to achieve F1 scores of 0.8062, 0.6445, 0.7302, 0.5671, 0.6555, 0.7545 and 0.6702 in English, Spanish, Portuguese, Hindi, Urdu, Mandarin and Turkish respectively achieving a rank of 5 in English and 7 on the remaining language tasks.

2. A novel multimodal dynamic fusion network for disfluency detection in spoken utterances

   Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar, Manan Suri, and Rajiv Ratn Shah

   arXiv preprint arXiv:2211.14700, Dec 2022
3. Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning

   Manan Suri

   In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), Jul 2022

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   This paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language Detection. Patronizing and Condescending Language (PCL) refers to language used with respect to vulnerable communities that portrays them in a pitiful way and is reflective of a sense of superiority. Task 4 involved binary classification (Subtask 1) and multi-label classification (Subtask 2) of Patronizing and Condescending Language (PCL). For our system, we experimented with fine-tuning different transformer-based pre-trained models including BERT, DistilBERT, RoBERTa and ALBERT. Further, we have used token separated metadata in order to improve our model by helping it contextualize different communities with respect to PCL. We faced the challenge of class imbalance, which we solved by experimenting with different class weighting schemes. Our models were effective in both subtasks, with the best performance coming out of models with Effective Number of Samples (ENS) class weighting and token separated metadata in both subtasks. For subtask 1 and subtask 2, our best models were finetuned BERT and RoBERTa models respectively.

4. Multimodal BERT for Identifying Claims in Tweets

   Manan Suri, Prajeet Katari, and Saumay Dudeja

   In Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, September 5th - to - 8th, 2022, Jul 2022