Who Cited It

A Decomposable Attention Model for Natural Language Inference

2016 · 1,377 citations · 5 from inside this corpus

Ankur P. Parikh, Oscar Täckström low, Dipanjan Das, Jakob Uszkoreit

We propose a simple neural architecture for natural language inference.Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable.On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information.Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.

A Decomposable Attention Model for Natural Language Inference (2016)A Decomposable Attention Mode…Long Short-Term Memory (1997)Long Short-Term MemoryDropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…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…Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (2010)Adaptive Subgradient Methods …Deep Sparse Rectifier Neural Networks (2011)Deep Sparse Rectifier Neural …AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at S… (2018)AI-Assisted Pipeline for Dyna…BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019)BERT: Pre-training of Deep Bi…Natural Questions: A Benchmark for Question Answering Research (2019)Natural Questions: A Benchmar…BERTScore: Evaluating Text Generation with BERT (2019)BERTScore: Evaluating Text Ge…Deep Learning--based Text Classification (2021)Deep Learning--based Text Cla…
11 of 11 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
this paper works it cites works citing it node size = global citations · hover for the full title

What this paper cites, inside the corpus

PaperYearCited
Long Short-Term Memory1997101,359
Dropout: a simple way to prevent neural networks from overfitting201434,236
Glove: Global Vectors for Word Representation201434,067
Neural Machine Translation by Jointly Learning to Align and Translate201414,615
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization20108,621
Deep Sparse Rectifier Neural Networks20115,428

What cites it, inside the corpus

PaperYearCited
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at S…201846,036
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding201933,416
Natural Questions: A Benchmark for Question Answering Research20192,095
BERTScore: Evaluating Text Generation with BERT20192,065
Deep Learning--based Text Classification20211,525

Links

DOI · Open access copy · OpenAlex record

Topics

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

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports33 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:59+00:00.

sha256 59007f057c7dae86…