The Open Virtual Assistant Lab seminar is a weekly event where students and researchers present their work in areas related to voice user interfaces, chatbots and virtual assistants. Topics include user interaction with natural language, chatbot-based applications, agent-to-agent distributed systems, question answering, natural language understanding and generation, and more.

The seminar is open to the Stanford community and members of the OVAL affiliate program. If you're interested to give a talk, please contact .

Mailing list: oval-seminar@lists.stanford.edu

Archive: Summer 2019, Fall 2019

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1/10: Organizational Lunch

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Location: Gates 463A (4th floor, B wing)

Organizational lunch. Come enjoy food and sign up to give a talk during the quarter.

1/17: Domain-Specific Question Answering for Conversational Systems

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Location: Gates 463A (4th floor, B wing)

Abstract:
Open-domain question answering (QA) is the task of answering natural questions from a large collection of documents. The typical open-domain QA system starts with information retrieval to select a subset of documents from the corpus, which are then processed by a reading comprehension model to select the answer spans. The majority of prior research on this topic focuses on answering questions from Wikipedia. However, searching for answers in a broad range of specialized domains ranging from IT infrastructure to health sciences remains a challenging problem since many of these domains lack extensive labeled datasets for training. In addition, their design usually favors accuracy, and latency and throughput are not major concerns.
In this talk, I will describe an open-domain QA system consisting of a new multi-stage pipeline, which employs a traditional information retriever, neural relevance feedback, a neural ranker, and a reading comprehension stage. This system substantially outperforms the previous state of the art for question answering on Wikipedia/SQuAD, and can be easily tuned to meet various timing requirements. I will also discuss how synthesized in-domain data enables an effective domain adaptation for such systems.

Speaker: Sina Jandaghi Semnani
Sina Semnani is a PhD candidate in the Electrical Engineering department at Stanford University, and holds a BS degree in Computer Science. He has previously worked on using machine learning tools in computer network design. He is interested in building systems that can extract knowledge from large amounts of unstructured data available in various domains. Additionally, his research interests include conversational systems and data-efficient deep learning.

1/24: Using Synthetized Data To Train Multi-Domain Dialogue State Trackers

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Location: Gates 463A (4th floor, B wing)

Abstract: Multi-domain dialogue state tracking is the task of tracking the domain of a conversation, the intent of the user, and which information has been provided in a task oriented dialogue. Current state of the art uses Wizard of Oz techniques to acquire data in the new domains, which is expensive and prone to human error. We instead propose a zero-shot training strategy in which real world data from other domains is combined with synthesized data in the new domain to bootstrap a dialogue state tracker. Our technique uses an abstract model of task oriented dialogues, which can be instantiated in different domains by providing the domain-specific lexicon, to synthesize a large set of dialogues and their turn-by-turn annotation. In our experiment in the MultiWOZ benchmark dataset, we can achieve between 63% and 93% of the accuracy of real world data, at a fraction of the cost. Our experiments show that pretrained language models (BERT) complement the synthesized data. I will also present work in progress in applying the technique to the ThingTalk programming language, to produce full multi-domain conversational agents for task-oriented dialogues with minimal conversational design.

Speaker: Giovanni Campagna

1/31: TBA

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Location: Gates 463A (4th floor, B wing)

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Speaker: Robin Jia

2/7: TBA

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Location: Gates 463A (4th floor, B wing)

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2/14: TBA

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Location: Gates 463A (4th floor, B wing)

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2/21: TBA

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Location: Gates 463A (4th floor, B wing)

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2/28: TBA

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Location: Gates 463A (4th floor, B wing)

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3/6: TBA

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Location: Gates 463A (4th floor, B wing)

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3/13: TBA

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Location: Gates 463A (4th floor, B wing)

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