YodaQA: A Modular Question Answering System Pipeline

41 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Simple and Effective Multi-Paragraph Reading Comprehension (2017, background): “Systems that try to answer questions using natural language resources such as YodaQA (Baudiˇs, 2015) typically use pipelined methods to retrieve related text, build answer candidates, and pick a ﬁnal output.”
Reading Wikipedia to Answer Open-Domain Questions (2017, background): “There are a number of highly developed full pipeline QA approaches using either the Web, as does QuASE (Sun et al., 2015), or Wikipedia as a resource, as do Microsoft’s AskMSR (Brill et al., 2002), IBM’s DeepQA (Ferrucci et al., 2010) and YodaQA (Baudi ˇ s, 2015; Baudi ˇ s and ˇSediv ` y, 2015) — th”
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering (2020, background): “Studies in the early stage (Kwok et al., 2001; Brill et al., 2002; Ferrucci et al., 2010; Baudiˇs, 2015) mostly rely on highly sophisticated pipelines and heterogeneous resources.”
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (2018, methodology): “4232 A naı̈ve option, in such a setting, is to take stateof-the-art QA systems developed for each source, and aggregate their predictions using some heuristic (Ferrucci et al., 2010; Baudiš, 2015).”