Papers by Anandhavelu N
Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models (2022.emnlp-main)
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| Challenge: | Existing methods to perform named entity recognition (NER) on unlabeled data are difficult to obtain in low-resource domains. |
| Approach: | They propose ways to use unlabeled data for pretraining to improve performance in downstream tasks. |
| Outcome: | The proposed methods outperform models trained on unlabeled data on seven domains. |
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus (2021.naacl-main)
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| Challenge: | Existing methods for style transfer require joint annotations across all stylistic dimensions, limiting their application to multiple styles. |
| Approach: | They initialize an encoder-decoder setup with transformer-based language model pre-trained on a generic corpus and enhance its re-writing capability to multiple target style dimensions by employing multiple style-aware language models as discriminators. |
| Outcome: | The proposed model can control styles across multiple style dimensions while preserving content of the input text. |
He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation (2021.findings-acl)
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Aparna Garimella, Akhash Amarnath, Kiran Kumar, Akash Pramod Yalla, Anandhavelu N, Niyati Chhaya, Balaji Vasan Srinivasan
| Challenge: | Existing studies have focused on mitigating social biases in context-free representations, with recent shift to contextual ones. |
| Approach: | They propose an approach to mitigate social biases in a large pre-trained contextual language model . they propose lexical co-occurrence-based bias penalization in the decoder units . |
| Outcome: | The proposed approach reduces biases in fill-in-the-blank sentences and summarizes . it also reduces the biased representations in the frameworks, the authors show . |
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly (2025.coling-main)
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Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber
| Challenge: | Existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s needs. |
| Approach: | They propose a framework that uses human-in-the-loop refinement to adapt to changing user questions. |
| Outcome: | The proposed framework is domain-agnostic, responsive, efficient for helping users access useful information while quickly reorganizing information in response to evolving information needs. |
ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring (2021.emnlp-main)
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| Challenge: | Contracts are a common type of legal document that frequent in business workflows, but there has been limited NLP research in understanding and generating them. |
| Approach: | They propose a task of clause recommendation to help automate contract authoring . they first predict if a specific clause type is relevant to be added in a contract . then they propose two-staged pipeline to recommend top clauses based on the contract context . |
| Outcome: | The proposed pipeline predicts if a clause type is relevant to be added in a contract and recommends the top clauses for the given type based on the contract context. |