Keynote: Past, Present, Future of Conversational AI

Recent advances in deep learning based methods for language processing, especially using self-supervised learning methods resulted in new excitement towards building more sophisticated Conversational AI systems. While this is partially true for social chatbots or retrieval-based applications, it is commonplace to see dialogue processing as yet another task while assessing these new state of the art approaches. In this talk, I will argue that Conversational AI comes with an orthogonal methodology for machine learning to complement such methods interacting with the users using implicit and explicit signals. This is an exceptional opportunity for Conversational AI research moving forward and I will present couple representative efforts from Alexa AI.

Keynote: Directions of Dialog Research in the Era of Big Pre-training Models

Big pre-training models (such as BERT and GPT3) have demonstrated excellent performances on various NLP tasks. Instruction tuning and prompting have enabled these models to shine in low-resource settings. The natural question is “Will big models solve dialog tasks?” This talk will first go through big models’ impact on several sub-topics within dialog systems (e.g. social chatbots, task-oriented dialog systems, negotiation/persuasion dialog systems, continue learning in dialog systems, multilingual dialog systems, multimodal dialog systems, deployable dialog systems, etc) and then follow up with the speaker's own interpretations of the challenges remaining and possible future directions.

Keynote: HybriDialogue: Towards Information-Seeking Dialogue Reasoning Grounded on Tabular and Textual Data

A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities. Previous work in multi-turn dialogue systems has primarily focused on either text or table information. In more realistic scenarios, having a joint understanding of both is critical as knowledge is typically distributed over both unstructured and structured forms. In this talk, I will present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions. We conduct several baseline experiments, including retrieval, system state tracking, and dialogue response generation. Our results show that there is still ample opportunity for improvement, demonstrating the importance of building stronger dialogue systems that can reason over the complex setting of information-seeking dialogue grounded on tables and text. I will also briefly mention a few related studies on dialogue research from the UCSB NLP Group.

Keynote: Scaling impact: the case for humanitarian NLP

Advances in core NLP capabilities have enabled an extensive variety of scenarios where conversational AI provides real value for companies and customers alike. Leveraging lessons learned from these successes to applying the technology in the humanitarian context requires an understanding of both the potential for impact and risk of misuse.

In this talk, we'll discuss how to leverage conversational AI to scale impact for audiences in the humanitarian sector while earning and maintaining trust with the adopters of the technology and with the people they impact.

Keynote: Dialog Management for Conversational Task-Oriented Industry Solutions

This talk will focus on how the Omilia Cloud Platform® leverages the notion of Dialog Act in order to solve real-life use cases in task-oriented dialog systems for call centers. We will address the challenge of completing tasks efficiently, achieving high KPIs and integrating with a call center, while at the same time building and maintaining a flexible conversational NLU system.