Q. Vera Liao


2022

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Human-Centered Evaluation of Explanations
Jordan Boyd-Graber | Samuel Carton | Shi Feng | Q. Vera Liao | Tania Lombrozo | Alison Smith-Renner | Chenhao Tan
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts

The NLP community are increasingly interested in providing explanations for NLP models to help people make sense of model behavior and potentially improve human interaction with models. In addition to computational challenges in generating these explanations, evaluations of the generated explanations require human-centered perspectives and approaches. This tutorial will provide an overview of human-centered evaluations of explanations. First, we will give a brief introduction to the psychological foundation of explanations as well as types of NLP model explanations and their corresponding presentation, to provide the necessary background. We will then present a taxonomy of human-centered evaluation of explanations and dive into depth in the two categories: 1) evaluation based on human-annotated explanations; 2) evaluation with human-subjects studies. We will conclude by discussing future directions. We will also adopt a flipped format to maximize the in- teractive components for the live audience.

2020

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Agent Assist through Conversation Analysis
Kshitij Fadnis | Nathaniel Mills | Jatin Ganhotra | Haggai Roitman | Gaurav Pandey | Doron Cohen | Yosi Mass | Shai Erera | Chulaka Gunasekara | Danish Contractor | Siva Patel | Q. Vera Liao | Sachindra Joshi | Luis Lastras | David Konopnicki
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.