Papers by Arvind Agarwal
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
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| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
BI-Bench : A Comprehensive Benchmark Dataset and Unsupervised Evaluation for BI Systems (2025.acl-industry)
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| Challenge: | Existing benchmarks focus on isolated components rather than addressing the broader needs of BI users. |
| Approach: | They propose a holistic, end-to-end benchmarking framework that categorizes queries into descriptive, diagnostic, predictive, and prescriptive types, aligning with practical BI needs. |
| Outcome: | The proposed framework assesses BI systems on quality, relevance, depth of insights based on queries categorized into descriptive, diagnostic, predictive, and prescriptive types . |
VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment (2021.emnlp-main)
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| Challenge: | State-of-the-art Ontology Alignment systems are based on domain-dependent approaches with handcrafted rules or domain-specific architectures, making them unscalable and inefficient. |
| Approach: | They propose a Deep Learning based model that exploits syntactic and semantic information encoded in ontologies by using a dual-attention mechanism. |
| Outcome: | The proposed model exploits syntactic and semantic information encoded in ontologies and is flexible and scalable to different domains with minimal effort. |
Development of an Enterprise-Grade Contract Understanding System (2021.naacl-industry)
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Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
| Challenge: | Currently, legal contract review remains an expensive and arduous process. |
| Approach: | They describe a commercial system designed and deployed for contract understanding that enables legal professionals to review contracts. |
| Outcome: | The proposed system is used by a wide range of enterprise users and solves three major challenges. |
Dialogue-act-driven Conversation Model : An Experimental Study (C18-1)
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| Challenge: | In the last decade, natural language processing and machine learning have come a long way towards building an automated dialogue system. |
| Approach: | They propose a way to encode dialogue act information and use it to build a model that can use it in a natural way. |
| Outcome: | The proposed model outperforms baseline models on a new daily dialogue dataset and achieves an MRR of about 84.8%. |
Goal-Driven Data Story, Narrations and Explanations (2025.naacl-industry)
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| Challenge: | Unlike existing tools, our system addresses the ambiguity of vague, multi-line queries, setting a new benchmark in data storytelling by tackling complexities no existing system comprehensively handles. |
| Approach: | They propose a system that processes and interprets vague, open-ended, and multi-line complex queries, transforming them into coherent, actionable data stories. |
| Outcome: | The proposed system processes and interprets vague, open-ended, and multi-line complex queries, transforming them into coherent, actionable data stories. |