Open-Domain Question Answering
173 papers with code • 10 benchmarks • 24 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
TriviaQA
WebQuestions
LAMA
SearchQA
ELI5
KILT
ScienceQA
DuReader
QUASAR-T
QUASAR
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.
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.
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
princeton-nlp/DensePhrases
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EACL 2021
Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge.
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.
