CS Seminar: João Sedoc – “Building and Evaluating Conversational Agents”
Hackerman Hall B-17
There has been a renewed focus on dialog systems, including non-task driven conversational agents (i.e. “chit-chat bots”). Dialog is a challenging problem since it spans multiple conversational turns. To further complicate the problem, there are many contextual cues and valid possible utterances. We propose that dialog is fundamentally a multiscale process, given that context is carried from previous utterances in the conversation. Deep learning dialog models, which are based on recurrent neural network (RNN) encoder-decoder sequence-to-sequence models, lack the ability to create temporal and stylistic coherence in conversations. João’s thesis focuses on novel neural models for topical and stylistic coherence and their evaluation.
João is a final year PhD student at the University of Pennsylvania, advised by Lyle Ungar. His PhD research focuses on Natural Language Generation, particularly deep learning methods for non-task driven conversational agents (chatbots) and the evaluation of these models. His research also includes work on word and sentence embeddings, word and verb predicate clustering, and multi-scale models. He is generally interested in Natural Language Processing, Time Series Analysis, and Deep Learning.
Benjamin Van Durme