DeepAI
Log In Sign Up

Revisiting the Open-Domain Question Answering Pipeline

09/02/2020
by   Sina J. Semnani, et al.
0

Open-domain question answering (QA) is the tasl of identifying answers to natural questions from a large corpus of documents. The typical open-domain QA system starts with information retrieval to select a subset of documents from the corpus, which are then processed by a machine reader to select the answer spans. This paper describes Mindstone, an open-domain QA system that consists of a new multi-stage pipeline that employs a traditional BM25-based information retriever, RM3-based neural relevance feedback, neural ranker, and a machine reading comprehension stage. This paper establishes a new baseline for end-to-end performance on question answering for Wikipedia/SQuAD dataset (EM=58.1, F1=65.8), with substantial gains over the previous state of the art (Yang et al., 2019b). We also show how the new pipeline enables the use of low-resolution labels, and can be easily tuned to meet various timing requirements.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/01/2018

Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering

Recently, open-domain question answering (QA) has been combined with mac...
06/07/2019

RankQA: Neural Question Answering with Answer Re-Ranking

The conventional paradigm in neural question answering (QA) for narrativ...
08/31/2017

R^3: Reinforced Reader-Ranker for Open-Domain Question Answering

In recent years researchers have achieved considerable success applying ...
01/01/2021

Reader-Guided Passage Reranking for Open-Domain Question Answering

Current open-domain question answering (QA) systems often follow a Retri...
10/29/2017

Simple and Effective Multi-Paragraph Reading Comprehension

We consider the problem of adapting neural paragraph-level question answ...
06/09/2016

Key-Value Memory Networks for Directly Reading Documents

Directly reading documents and being able to answer questions from them ...
09/08/2021

R2-D2: A Modular Baseline for Open-Domain Question Answering

This work presents a novel four-stage open-domain QA pipeline R2-D2 (Ran...