Open-Domain Question Answering
206 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
WebQuestions
LAMA
ScienceQA
SearchQA
ELI5
KILT
DuReader
Most implemented papers
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
huggingface/transformers
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ACL 2020
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
Bidirectional Attention Flow for Machine Comprehension
Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.
Dense Passage Retrieval for Open-Domain Question Answering
facebookresearch/DPR
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EMNLP 2020
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.
Reformer: The Efficient Transformer
google/trax
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ICLR 2020
Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences.
Reading Wikipedia to Answer Open-Domain Questions
facebookresearch/DrQA
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ACL 2017
This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article.
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
jhyuklee/DensePhrases
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EACL 2021
Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge.
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article.
Generating Long Sequences with Sparse Transformers
openai/sparse_attention
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Preprint 2019
Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length.
SpanBERT: Improving Pre-training by Representing and Predicting Spans
facebookresearch/SpanBERT
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TACL 2020
We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text.
REALM: Retrieval-Augmented Language Model Pre-Training
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering.
