Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
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this paper
works it cites
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Citation edge evidence
- Hierarchical Attention Networks for Document Classification→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Get To The Point: Summarization with Pointer-Generator Networks→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Deep learning for healthcare: review, opportunities and challenges→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- fairseq: A Fast, Extensible Toolkit for Sequence Modeling→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Attention-based LSTM for Aspect-level Sentiment Classification→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Know What You Don’t Know: Unanswerable Questions for SQuAD→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Natural Questions: A Benchmark for Question Answering Research→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Reading Wikipedia to Answer Open-Domain Questions→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Teaching Machines to Read and Comprehend→Long Short-Term MemoryLegacy edge — source detail unavailable
- Teaching Machines to Read and Comprehend→Sequence to Sequence Learning with Neural NetworksLegacy edge — source detail unavailable
- Teaching Machines to Read and Comprehend→Understanding natural languageLegacy edge — source detail unavailable
- Hierarchical Attention Networks for Document Classification→Long Short-Term MemoryLegacy edge — source detail unavailable
- Attention-based LSTM for Aspect-level Sentiment Classification→Long Short-Term MemoryLegacy edge — source detail unavailable
- Get To The Point: Summarization with Pointer-Generator Networks→Abstractive Text Summarization using Sequence-to-sequence RNNs and BeyondLegacy edge — source detail unavailable
- Deep learning for healthcare: review, opportunities and challenges→Long Short-Term MemoryLegacy edge — source detail unavailable
- Natural Questions: A Benchmark for Question Answering Research→Reading Wikipedia to Answer Open-Domain QuestionsLegacy edge — source detail unavailable
- Natural Questions: A Benchmark for Question Answering Research→Know What You Don’t Know: Unanswerable Questions for SQuADLegacy edge — source detail unavailable
- fairseq: A Fast, Extensible Toolkit for Sequence Modeling→Get To The Point: Summarization with Pointer-Generator NetworksLegacy edge — source detail unavailable
- fairseq: A Fast, Extensible Toolkit for Sequence Modeling→Abstractive Text Summarization using Sequence-to-sequence RNNs and BeyondLegacy edge — source detail unavailable
- Sequence to Sequence Learning with Neural Networks→Long Short-Term MemoryLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Get To The Point: Summarization with Pointer-Generator NetworksLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Abstractive Text Summarization using Sequence-to-sequence RNNs and BeyondLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Long Short-Term Memory | 1997 | 101,359 |
| Sequence to Sequence Learning with Neural Networks | 2014 | 3,514 |
| Understanding natural language | 1972 | 2,846 |
What cites it, inside the corpus
Links
Download BibTeX · Download RIS · Open access copy · OpenAlex record
Topics
| Topic Modeling | Computer Science |
| Natural Language Processing Techniques | Computer Science |
| Advanced Text Analysis Techniques | Computer Science |
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- supports7 author record(s) attached.
- supports16 reference(s) recorded.
- neutralThe DOI carries no year to check against.
- supportsA title is present.
Provenance
sha256 930f5bc64fea8a60…