Natural Questions
71 papers with code • 2 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Natural Questions
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Libraries
Use these libraries to find Natural Questions models and implementations
BIG-bench
Belebele
QAMPARI
TheoremQA
Most implemented papers
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.
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.
RealFormer: Transformer Likes Residual Attention
google-research/google-research
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Findings (ACL) 2021
Transformer is the backbone of modern NLP models.
A BERT Baseline for the Natural Questions
This technical note describes a new baseline for the Natural Questions.
Event Extraction by Answering (Almost) Natural Questions
The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments.
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
stanford-oval/genie-toolkit
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EMNLP 2020
To demonstrate the generality of AutoQA, we also apply it to the Overnight dataset.
Generating Natural Questions About an Image
chingyaoc/vqg-tensorflow
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ACL 2016
There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images.
TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
Additionally, we show that the transfer step of TANDA makes the adaptation step more robust to noise.
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets.
