Revisiting the Open-Domain Question Answering Pipeline
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalMachine Reading ComprehensionNatural QuestionsOpen-Domain Question AnsweringQuestion AnsweringReading ComprehensionRetrievalSimilar Papers 제목 키워드 기반
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents. Recently, th…
Machine Reading ComprehensionOpen-Domain Question AnsweringQuestion AnsweringReading ComprehensionA Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering
Speech-based open-domain question answering (QA over a large corpus of text passages with spoken questions) has emerged as an important task due to the increasing number of users interacting with QA systems via speech in…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Open-Domain Question AnsweringPassage Retrieval+4SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering
Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and is often difficult to reproduce previous results due to complex …
Open-Domain Question AnsweringQuestion AnsweringSynthetic Data Generation for Multilingual Domain-Adaptable Question Answering Systems
Deep learning models have significantly advanced the state of the art of question answering systems. However, the majority of datasets available for training such models have been annotated by humans, are open-domain, an…
Question AnsweringSynthetic Data GenerationAn Open-Domain QA System for e-Governance
The paper presents an open-domain Question Answering system for Romanian, answering COVID-19 related questions. The QA system pipeline involves automatic question processing, automatic query generation, web searching for…
Open-Domain Question AnsweringQuestion Answering