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Q. Vera Liao

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3 papers

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AAAI Conference 2021 System Paper

Doc2Bot: Document grounded Bot Framework

Kshitij Fadnis
Pankaj Dhoolia
Li Zhu
Q. Vera Liao
Steven Ross
Nathaniel Mills
Sachindra Joshi
Luis Lastras

Conversational agents – or chatbots – are widely used to provide customer care and other informational support. Currently, the development of chatbots using standard frameworks requires a lot of manual crafting by subject matter experts (SMEs). On the other hand, while learning-based approaches to dialog have made significant advancements, they require training with a large volume of dialog data, which chatbot developers typically do not have access to. To tackle these challenges, we introduce DOC2BOT, a system that supports the automated construction of chatbots by digesting various forms of documents such as business manuals, HowTos, and customer support pages that organizations own. In addition to this, DOC2BOT provides a user-friendly experience to SMEs, and minimizes the effort expended by them by supporting intuitive interactions and streamlining their workflow.

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JMLR Journal 2020 Journal Article

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models

Vijay Arya
Rachel K. E. Bellamy
Pin-Yu Chen
Amit Dhurandhar
Michael Hind
Samuel C. Hoffman
Stephanie Houde
Q. Vera Liao

As artificial intelligence algorithms make further inroads in high-stakes societal applications, there are increasing calls from multiple stakeholders for these algorithms to explain their outputs. To make matters more challenging, different personas of consumers of explanations have different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360, an open-source Python toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of interpretation and explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. The toolkit is not only the software, but also guidance material, tutorials, and an interactive web demo to introduce AI explainability to different audiences. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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AAAI Conference 2020 System Paper

Doc2Dial: A Framework for Dialogue Composition Grounded in Documents

Song Feng
Kshitij Fadnis
Q. Vera Liao
Luis A. Lastras

We introduce DOC2DIAL, an end-to-end framework for generating conversational data grounded in given documents. It takes the documents as input and generates the pipelined tasks for obtaining the annotations speciﬁcally for producing the simulated dialog ﬂows. Then, the dialog ﬂows are used to guide the collection of the utterances via the integrated crowdsourcing tool. The outcomes include the human-human dialogue data grounded in the given documents, as well as various types of automatically or human labeled annotations that help ensure the quality of the dialog data with the ﬂexibility to (re)composite dialogues. We expect such data can facilitate building automated dialogue agents for goal-oriented tasks. We demonstrate DOC2DIAL system with the various domain documents for customer care.

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