Natural Questions
71 papers with code • 2 benchmarks • 4 datasets
Libraries
Use these libraries to find Natural Questions models and implementationsMost 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
Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge.
Relevance-guided Supervision for OpenQA with ColBERT
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
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
To demonstrate the generality of AutoQA, we also apply it to the Overnight dataset.
Generating Natural Questions About an Image
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.