Papers by Deeksha Varshney
Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization (2025.acl-long)
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| Challenge: | Existing NLP methods lack robustness against greenwashed ESG content . existing methods often extract insights that reflect misleading or exaggerated sustainability claims rather than objective ESG performance. |
| Approach: | They propose to use a dataset to improve the robustness of ESG analysis amid the prevalence of greenwashing to analyze sustainability reports. |
| Outcome: | The proposed model improves robustness against greenwashed claims rather than objective ESG performance. |
Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation (2025.emnlp-main)
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| Challenge: | Existing methods for forward counterfactual generation face limitations . large language models (LLMs) offer promise but remain unexplored for this application . |
| Approach: | They propose a benchmark to support forward counterfactual generation in finance . they use financial news headlines to curate financial news and provide structured evaluation . |
| Outcome: | The proposed benchmark aims to provide scalable, automated insights into potential market opportunities and risks for stakeholders. |
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)
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| Challenge: | Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences. |
| Approach: | They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion. |
| Outcome: | The proposed model outperforms baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses. |
Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation (2022.naacl-main)
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| Challenge: | Empirical results show that our proposed model outperforms the state-of-the-art methods in terms of both automatic evaluation metrics and human judgment. |
| Approach: | They propose a model which uses large-scale commonsense and named entity based knowledge to ground dialogue on external knowledge and topic-specific knowledge associated with each utterance. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on two benchmark datasets. |
CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation (2022.emnlp-main)
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| Challenge: | Prior research has provided a single poorly graded label for the entire utterance, which may mislead model training and/or lead to erroneous assessment. |
| Approach: | They propose to use telemedicine to carry on a natural conversation and understand the meanings of words to respond with a coherent dialog. |
| Outcome: | telemedicine has been shown to be effective in carrying on a natural conversation and understanding the meanings of words to respond with a coherent dialog. |
Knowledge-enhanced Response Generation in Dialogue Systems: Current Advancements and Emerging Horizons (2024.lrec-tutorials)
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| Challenge: | Knowledge-enhanced Dialogue Systems (KEDS) are a new approach to enhancing human-machine interaction through natural language. |
| Approach: | This tutorial provides an in-depth exploration of Knowledge-enhanced Dialogue Systems (KEDS) it aims to elucidate their significance, highlight advances made using deep learning, and pinpoint the current challenges. |
| Outcome: | The tutorial aims to give attendees a comprehensive understanding of KEDS, and highlight advances made using deep learning and pinpoint the current challenges. |