@article{liu_mei_peng_vatsavai_2023, title={Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent}, ISSN={["2375-9232"]}, DOI={10.1109/ICDMW60847.2023.00196}, abstractNote={Conversational agents leveraging AI, particularly deep learning, are emerging in both academic research and real-world applications. However, these applications still face challenges, including disrespecting knowledge and facts, not personalizing to user preferences, and enormous demand for computational resources during training and inference. Recent research efforts have been focused on addressing these challenges from various aspects, including supplementing various types of auxiliary information to the conversational agents. However, existing methods are still not able to effectively and efficiently exploit relevant information from these auxiliary supplements to further unleash the power of the conversational agents and the language models they use. In this paper, we present a novel method, PK-NCLI, that is able to accurately and efficiently identify relevant auxiliary information to improve the quality of conversational responses by learning the relevance among persona, chat history, and knowledge background through lowlevel normalized contextual latent interaction. Our experimental results indicate that PK-NCLI outperforms the state-of-theart method, PK-FoCus, by 47.80%/30.61%/24.14% in terms of perplexity, knowledge grounding, and training efficiency, respectively, and maintained the same level of persona grounding performance. We also provide a detailed analysis of how different factors, including language model choices and trade-offs on training weights, would affect the performance of PK-NCLI.}, journal={2023 23RD IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS, ICDMW 2023}, author={Liu, Junfeng and Mei, Zhuocheng and Peng, Kewen and Vatsavai, Ranga Raju}, year={2023}, pages={1543–1550} }
 @article{liu_symons_vatsavai_2023, title={Persona-Coded Poly-Encoder: Persona-Guided Multi-Stream Conversational Sentence Scoring}, ISSN={["1082-3409"]}, DOI={10.1109/ICTAI59109.2023.00044}, abstractNote={Recent advances in machine learning and deep learning have led to the widespread use of Conversational AI in many practical applications. However, it is still very challenging to leverage auxiliary information that can provide conversational context or personalized tuning to improve the quality of conversations. For example, there has only been limited research on using an individual’s persona information to improve conversation quality, and even state-of-the-art conversational AI techniques are unable to effectively leverage signals from heterogeneous sources of auxiliary data, such as multi-modal interaction data, demographics, SDOH data, etc. In this paper, we present a novel Persona-Coded Poly-Encoder method that leverages persona information in a multi-stream encoding scheme to improve the quality of response generation for conversations. To show the efficacy of the proposed method, we evaluate our method on two different persona-based conversational datasets, and compared against two state-of-the-art methods. Our experimental results and analysis demonstrate that our method can improve conversation quality over the baseline method Poly-Encoder by $ 3.32\%$ and $ 2.94\%$ in terms of BLEU score and HR@1, respectively. More significantly, our method offers a path to better utilization of multi-modal data in conversational tasks. Lastly, our study outlines several challenges and future research directions for advancing personalized conversational AI technology.}, journal={2023 IEEE 35TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAI}, author={Liu, Junfeng and Symons, Christopher and Vatsavai, Ranga Raju}, year={2023}, pages={250–257} }
 @article{liu_symons_vatsavai_2022, title={Persona-Based Conversational AI: State of the Art and Challenges}, ISSN={["2375-9232"]}, DOI={10.1109/ICDMW58026.2022.00129}, abstractNote={Conversational AI has become an increasingly prominent and practical application of machine learning. How-ever, existing conversational AI techniques still suffer from var-ious limitations. One such limitation is a lack of well-developed methods for incorporating auxiliary information that could help a model understand conversational context better. In this paper, we explore how persona-based information could help improve the quality of response generation in conversations. First, we provide a literature review focusing on the current state-of-the-art methods that utilize persona information. We evaluate two strong baseline methods, the Ranking Profile Memory Network and the Poly-Encoder, on the NeurIPS ConvAI2 benchmark dataset. Our analysis elucidates the importance of incorporating persona information into conversational systems. Additionally, our study highlights several limitations with current state-of-the-art meth-ods and outlines challenges and future research directions for advancing personalized conversational AI technology.}, journal={2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS, ICDMW}, author={Liu, Junfeng and Symons, Christopher and Vatsavai, Ranga Raju}, year={2022}, pages={993–1001} }