research

publications

Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support

Karthik Raja Kalaiselvi Bhaskar, Stephen Obadinma, et al.

ACL Anthology - EMNLP 2022, Industry Track · 2022

Abstract

Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges.

Paper Code

@inproceedings{obadinma-etal-2022-bringing,
    title = "Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support",
    author = "Obadinma, Stephen and Khan Khattak, Faiza and Wang, Shirley and Sidhorn, Tania and Lau, Elaine and Robertson, Sean and Niu, Jingcheng and Au, Winnie and Munim, Alif and Kalaiselvi Bhaskar, Karthik Raja",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-industry.44",
    doi = "10.18653/v1/2022.emnlp-industry.44",
    pages = "440--450",
}

Implicit Feedback Deep Collaborative Filtering Product Recommendation System

Karthik Raja Kalaiselvi Bhaskar, Deepa Kundur, Yuri Lawryshyn

arXiv · 2020

Abstract

In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviours. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. CF with Neural Collaborative Filtering (NCF) was shown to produce the highest Normalized Discounted Cumulative Gain (NDCG) performance on the real-world proprietary dataset provided by a large parts supply company.

Paper

@article{raja2020implicit,
    title={Implicit Feedback Deep Collaborative Filtering Product Recommendation System},
    author={Raja Kalaiselvi Bhaskar, Karthik and Kundur, Deepa and Lawryshyn, Yuri},
    journal={arXiv e-prints},
    pages={arXiv--2009},
    year={2020}
}

talks

Deep Learning in HealthCare and its Practical Limitations

AISC Jan 20, 2021 Virtual Talk

Deep learning uses statistical techniques to give computer systems the ability to learn with incoming data and to identify patterns and make decisions with minimal human direction. Armed with such targeted analytics, doctors may be better able to assess risk, make correct diagnoses, and offer patients more effective treatments. Deep Learning has a lot of potential in Healthcare. But why don't these techniques are adopted in hospitals yet? What are the gaps between academic research and production level code in Deep Learning and Healthcare?

History of Deep LearningWhy Deep Learning for Healthcare?Practical LimitationsResearch vs ProductionData AugmentationData SynthesisPretrainingDeep Learning EngineeringML Lifecycle

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