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Open-Domain Question Answering

32 papers with code · Natural Language Processing
Subtask of Question Answering

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

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Greatest papers with code

Reading Wikipedia to Answer Open-Domain Questions

ACL 2017 facebookresearch/ParlAI

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.

OPEN-DOMAIN QUESTION ANSWERING READING COMPREHENSION

Bidirectional Attention Flow for Machine Comprehension

5 Nov 2016allenai/bi-att-flow

Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.

OPEN-DOMAIN QUESTION ANSWERING READING COMPREHENSION

Dense Passage Retrieval for Open-Domain Question Answering

10 Apr 2020deepset-ai/haystack

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.

OPEN-DOMAIN QUESTION ANSWERING

REALM: Retrieval-Augmented Language Model Pre-Training

10 Feb 2020deepset-ai/haystack

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

LANGUAGE MODELLING OPEN-DOMAIN QUESTION ANSWERING

Language Models as Knowledge Bases?

IJCNLP 2019 facebookresearch/LAMA

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks.

LANGUAGE MODELLING OPEN-DOMAIN QUESTION ANSWERING

Deep Learning for Answer Sentence Selection

4 Dec 2014brmson/dataset-sts

Answer sentence selection is the task of identifying sentences that contain the answer to a given question.

FEATURE ENGINEERING OPEN-DOMAIN QUESTION ANSWERING

ktrain: A Low-Code Library for Augmented Machine Learning

19 Apr 2020amaiya/ktrain

We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply.

IMAGE CLASSIFICATION LINK PREDICTION NODE CLASSIFICATION OPEN-DOMAIN QUESTION ANSWERING TEXT CLASSIFICATION

SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

18 Apr 2017google/active-qa

We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering.

OPEN-DOMAIN QUESTION ANSWERING READING COMPREHENSION