Open-Domain Question Answering
195 papers with code • 15 benchmarks • 26 datasets
Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
Benchmarks
These leaderboards are used to track progress in Open-Domain Question Answering
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Libraries
Use these libraries to find Open-Domain Question Answering models and implementations
SQuAD
Natural Questions
TriviaQA
LAMA
WebQuestions
ScienceQA
SearchQA
ELI5
KILT
DuReader
Most implemented papers
Reducing Transformer Depth on Demand with Structured Dropout
Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering.
Relevance-guided Supervision for OpenQA with ColBERT
stanford-futuredata/ColBERT
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In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passages.
Gated-Attention Readers for Text Comprehension
In this paper we study the problem of answering cloze-style questions over documents.
Break It Down: A Question Understanding Benchmark
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer.
ktrain: A Low-Code Library for Augmented Machine Learning
amaiya/ktrain
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We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply.
Distilling Knowledge from Reader to Retriever for Question Answering
facebookresearch/FiD
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ICLR 2021
A challenge of using such methods is to obtain supervised data to train the retriever model, corresponding to pairs of query and support documents.
Learning Dense Representations of Phrases at Scale
jhyuklee/DensePhrases
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ACL 2021
Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space.
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
nyu-dl/SearchQA
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We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering.
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering
nijianmo/arc-etrr-code
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NAACL 2019
In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process.
