This week we will have two presentations from CLIP Lab members. Please see below for their abstracts:
Question answering (QA) is one of the most important and challenging tasks for understanding human language. With the help of large-scale benchmarks, state-of-the-art neural methods have made significant progress to even answer complex questions that require multiple evidence pieces. Nevertheless, training existing SOTA models requires several assumptions (e.g., intermediate evidence annotation, corpus semi-structure) that limit the applicability to only academic testbeds. In this talk, I discuss several solutions to make current QA systems more practical.
I first describe a state-of-the-art system for complex QA, then I introduce a dense retrieval approach that iteratively forms an evidence chain through beam search in dense representations, without using semi-structured information. Finally, I describe a dense retrieval work that focuses on a weakly-supervised setting, by learning to find evidence from a large corpus, and relying only on distant supervision for model training.
Suraj Nair is a Ph.D. student at the Department of Computer Science advised by Douglas W. Oard. His research focuses on building models for effective and efficient retrieval across languages.
Chen Zhao is a sixth-year PhD candidate at CLIP, co-advised by Prof. Jordan Boyd-Graber and Prof. Hal Daumé III. His research interests lie in question answering, including knowledge representation from large text corpora for complex QA, and semantic parsing over tables.