Open-Domain Question Answering

133 papers with code • 14 benchmarks • 23 datasets

Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.

Libraries

Use these libraries to find Open-Domain Question Answering models and implementations

Most implemented papers

Bidirectional Attention Flow for Machine Comprehension

allenai/bi-att-flow 5 Nov 2016

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 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 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 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

huggingface/transformers 10 Nov 2019

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 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 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 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

google-research/language 10 Feb 2020

Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering.

Gated-Attention Readers for Text Comprehension

bdhingra/ga-reader ACL 2017

In this paper we study the problem of answering cloze-style questions over documents.