scholarly journals XQA: A Cross-lingual Open-domain Question Answering Dataset

2019 ◽  
Author(s):  
Jiahua Liu ◽  
Yankai Lin ◽  
Zhiyuan Liu ◽  
Maosong Sun
Keyword(s):  
Question Answering ◽  
Open Domain ◽  
Cross Lingual

scholarly journals MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

2021 ◽  
Vol 9 ◽  
pp. 1389-1406
Author(s):  
Shayne Longpre ◽  
Yi Lu ◽  
Joachim Daiber
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Linguistically Diverse ◽  
Data Representation ◽  
Independent Data ◽  
Open Domain ◽  
Low Resource ◽  
Art Methods ◽  
Questions And Answers ◽  
Cross Lingual

Abstract Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open- domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on heavily curated, language- independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state- of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages.1

scholarly journals Don’t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering

2020 ◽  
Author(s):  
Yuxiang Wu ◽  
Pasquale Minervini ◽  
Pontus Stenetorp ◽  
Sebastian Riedel
Keyword(s):  
Question Answering ◽  
Open Domain ◽  
Adaptive Computation

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

2021 ◽  
Author(s):  
Martin Fajcik ◽  
Martin Docekal ◽  
Karel Ondrej ◽  
Pavel Smrz
Keyword(s):  
Question Answering ◽  
Open Domain

scholarly journals Relevance-guided Supervision for OpenQA with ColBERT

2021 ◽  
Vol 9 ◽  
pp. 929-944
Author(s):  
Omar Khattab ◽  
Christopher Potts ◽  
Matei Zaharia
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Training Data ◽  
Coarse Grained ◽  
Retrieval Model ◽  
Open Domain ◽  
Weak Supervision ◽  
Fine Grained ◽  
Vector Representations ◽  
Large Corpus

Abstract Systems for Open-Domain Question Answering (OpenQA) generally depend on a retriever for finding candidate passages in a large corpus and a reader for extracting answers from those passages. In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passages. We argue that this modeling choice is insufficiently expressive for dealing with the complexity of natural language questions. To address this, we define ColBERT-QA, which adapts the scalable neural retrieval model ColBERT to OpenQA. ColBERT creates fine-grained interactions between questions and passages. We propose an efficient weak supervision strategy that iteratively uses ColBERT to create its own training data. This greatly improves OpenQA retrieval on Natural Questions, SQuAD, and TriviaQA, and the resulting system attains state-of-the-art extractive OpenQA performance on all three datasets.

Sign in / Sign up

Export Citation Format

Share Document