Open-Domain Question Answering
197 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
LAMA
WebQuestions
ScienceQA
SearchQA
ELI5
KILT
DuReader
Most implemented papers
Latent Retrieval for Weakly Supervised Open Domain Question Answering
google-research/language
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ACL 2019
We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system.
ELI5: Long Form Question Answering
facebookresearch/ELI5
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ACL 2019
We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions.
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models.
KILT: a Benchmark for Knowledge Intensive Language Tasks
We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community.
What Makes Good In-Context Examples for GPT-$3$?
stanfordnlp/dsp
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Inspired by the recent success of leveraging a retrieval module to augment large-scale neural network models, we propose to retrieve examples that are semantically-similar to a test sample to formulate its corresponding prompt.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.
Answer Complex Questions: Path Ranker Is All You Need
mindspore-ai/models
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Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021
Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates.
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
stanford-futuredata/ColBERT
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NAACL 2022
Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks.
