Who Cited It

Reading Wikipedia to Answer Open-Domain Questions

2017 · 1,454 citations · 4 from inside this corpus

Danqi Chen, Adam Fisch low, Jason Weston, Antoine Bordes

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. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA datasets indicate that (1) both modules are highly competitive with respect to existing counterparts and (2) multitask learning using distant supervision on their combination is an effective complete system on this challenging task.

Reading Wikipedia to Answer Open-Domain Questions (2017)Reading Wikipedia to Answer O…Adam: A Method for Stochastic Optimization (2014)Adam: A Method for Stochastic…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…Glove: Global Vectors for Word Representation (2014)Glove: Global Vectors for Wor…Neural Machine Translation by Jointly Learning to Align and Translate (2014)Neural Machine Translation by…The Stanford CoreNLP Natural Language Processing Toolkit (2014)The Stanford CoreNLP Natural …SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016)SQuAD: 100,000+ Questions for…A unified architecture for natural language processing (2008)A unified architecture for na…Distant supervision for relation extraction without labeled data (2009)Distant supervision for relat…Teaching Machines to Read and Comprehend (2015)Teaching Machines to Read and…Semantic Parsing on Freebase from Question-Answer Pairs (2013)Semantic Parsing on Freebase …Building Watson: An Overview of the DeepQA Project (2010)Building Watson: An Overview …Teaching Machines to Read and Comprehend (2015)Teaching Machines to Read and…Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Met… (2026)Affordance-Compiled Intellige…Natural Questions: A Benchmark for Question Answering Research (2019)Natural Questions: A Benchmar…Language Models as Knowledge Bases? (2019)Language Models as Knowledge …HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering (2018)HotpotQA: A Dataset for Diver…
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What this paper cites, inside the corpus

PaperYearCited
Adam: A Method for Stochastic Optimization201484,698
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
Glove: Global Vectors for Word Representation201434,067
Neural Machine Translation by Jointly Learning to Align and Translate201414,615
The Stanford CoreNLP Natural Language Processing Toolkit20147,269
SQuAD: 100,000+ Questions for Machine Comprehension of Text20166,435
A unified architecture for natural language processing20085,207
Distant supervision for relation extraction without labeled data20092,924
Teaching Machines to Read and Comprehend20151,939
Semantic Parsing on Freebase from Question-Answer Pairs20131,618
Building Watson: An Overview of the DeepQA Project20101,523
Teaching Machines to Read and Comprehend20151,519

What cites it, inside the corpus

PaperYearCited
Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Met…20263,059
Natural Questions: A Benchmark for Question Answering Research20192,095
Language Models as Knowledge Bases?20191,798
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering20181,743

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Topics

Topic ModelingComputer Science
Multimodal Machine Learning ApplicationsComputer Science
Natural Language Processing TechniquesComputer Science

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  • supports4 author record(s) attached.
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