---
title: "Ask Me Anything — Kumar 2015"
description: "The authors argue that most NLP problems can be reframed as answering a question about some input text, whether the task is tagging, classification…"
authors: ["Ankit Kumar", "Ozan Irsoy", "Peter Ondruska", "Mohit Iyyer", "James Bradbury", "Ishaan Gulrajani", "Victor Zhong", "Romain Paulus", "Richard Socher"]
year: 2015
venue: "arXiv:1506.07285"
source: "https://arxiv.org/abs/1506.07285"
doi: "10.48550/arxiv.1506.07285"
arxiv: "1506.07285"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/kumar-2015-ask-me-anything-dynamic-memory/"
---
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# Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

**Authors:** Ankit Kumar, [Ozan Irsoy](https://md.research.elamur.ai/free/en/authors/ozan-irsoy/), Peter Ondruska, [Mohit Iyyer](https://md.research.elamur.ai/free/en/authors/mohit-iyyer/), [James Bradbury](https://md.research.elamur.ai/free/en/authors/james-bradbury/), [Ishaan Gulrajani](https://md.research.elamur.ai/free/en/authors/ishaan-gulrajani/), [Victor Zhong](https://md.research.elamur.ai/free/en/authors/victor-zhong/), [Romain Paulus](https://md.research.elamur.ai/free/en/authors/romain-paulus/), [Richard Socher](https://md.research.elamur.ai/free/en/authors/richard-socher/) · **Year:** 2015 · **Venue:** arXiv:1506.07285 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1506.07285) · [PDF](https://arxiv.org/pdf/1506.07285) · [DOI 10.48550/arxiv.1506.07285](https://doi.org/10.48550/arxiv.1506.07285) · [arXiv 1506.07285](https://arxiv.org/abs/1506.07285) · [OpenAlex](https://openalex.org/W2131494463)

**Citations:** 607 citations (openalex-snapshot · 2026-10-14) · [168](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The authors argue that most NLP problems can be reframed as answering a question about some input text, whether the task is tagging, classification, translation or coreference. They propose one neural architecture for all of them, the Dynamic Memory Network (DMN). It has four parts:
- an input module that encodes text with a GRU,
- a question module that reuses the same word embeddings,
- an episodic memory module that repeatedly scans the encoded facts using gated attention, conditioned on the question and its current memory,
- an answer module, another GRU, that emits output once or at every word.

The system is trained end-to-end using only word vectors and input–question–answer triplets. The paper reports state-of-the-art results on Facebook's bAbI QA tasks, the Stanford Sentiment Treebank and WSJ-PTB part-of-speech tagging. Several memory passes are essential for multi-step reasoning in bAbI, help slightly for sentiment and barely matter for POS tagging. The work is an early step toward a single general model for many NLP tasks.

## Key points
- The architecture uses GRUs throughout. LSTMs gave similar accuracy at higher cost, and both clearly beat a plain tanh RNN.
- Attention is a two-layer feed-forward gate over rich similarity features between each fact, the memory and the question. On bAbI, where supporting facts are annotated, the gates also receive supervised training.
- The memory is initialised with the question vector. An appended end-of-passes token tells the model when to stop iterating; datasets without gate supervision use a fixed maximum number of passes.
- On the Stanford Sentiment Treebank, two passes beat one or zero. The second pass sharpens attention, for example shifting weight from "best" to "lukewarm" in context. Three passes overfit.
- For POS tagging on WSJ-PTB, accuracy differs by less than 0.1 across different numbers of passes.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/)

## Cited by

Cited by

168 papers of our library cite it (of the 140673 whose bibliographies we read); 23 of them have a page here, 145 not yet.

- [Generating Sentences from a Continuous Space](https://md.research.elamur.ai/free/en/papers/bowman-2016-generating-sentences-continuous-space/)
- [A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task](https://md.research.elamur.ai/free/en/papers/chen-2016-thorough-examination-cnn-daily-mail/)
- [Recurrent Attention Network on Memory for Aspect Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/chen-2017-recurrent-attention-network-memory-aspect/)
- [Long Short-Term Memory-Networks for Machine Reading](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/)
- [A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks](https://md.research.elamur.ai/free/en/papers/hashimoto-2016-joint-many-task-model-growing/)
- [Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos](https://md.research.elamur.ai/free/en/papers/hazarika-2018-conversational-memory-network-emotion-recognition/)
- [ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection](https://md.research.elamur.ai/free/en/papers/hazarika-2018-icon-interactive-conversational-memory-network/)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://md.research.elamur.ai/free/en/papers/hill-2015-goldilocks-principle-reading-children-s/)
- [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)
- [Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds](https://md.research.elamur.ai/free/en/papers/labutov-2018-multi-relational-question-answering-narratives/)
- [Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention](https://md.research.elamur.ai/free/en/papers/liu-2016-recognizing-implicit-discourse-relations-repeated/)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://md.research.elamur.ai/free/en/papers/lu-2022-fantastically-ordered-prompts-where-find/)
- [Learned in Translation: Contextualized Word Vectors](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
- [Pointer Sentinel Mixture Models](https://md.research.elamur.ai/free/en/papers/merity-2016-pointer-sentinel-mixture-models/)
- [Learning to Generate Reviews and Discovering Sentiment](https://md.research.elamur.ai/free/en/papers/radford-2017-learning-generate-reviews-discovering-sentiment/)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://md.research.elamur.ai/free/en/papers/tang-2016-aspect-level-sentiment-classification-deep/)
- [Machine Comprehension Using Match-LSTM and Answer Pointer](https://md.research.elamur.ai/free/en/papers/wang-2016-machine-comprehension-using-match-lstm/)
- [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://md.research.elamur.ai/free/en/papers/welbl-2018-constructing-datasets-multi-hop-reading/)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://md.research.elamur.ai/free/en/papers/weston-2015-towards-ai-complete-question-answering/)
- [Hierarchical Attention Networks for Document Classification](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)
- [Chinese Zero Pronoun Resolution with Deep Memory Network](https://md.research.elamur.ai/free/en/papers/yin-2017-chinese-zero-pronoun-resolution-deep/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{kumar2015ask,
  title = {{Ask Me Anything: Dynamic Memory Networks for Natural Language Processing}},
  author = {Kumar, Ankit and Irsoy, Ozan and Ondruska, Peter and Iyyer, Mohit and Bradbury, James and Gulrajani, Ishaan and Zhong, Victor and Paulus, Romain and Socher, Richard},
  year = {2015},
  doi = {10.48550/arxiv.1506.07285},
  eprint = {1506.07285},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1506.07285}
}
```

- [← previous Arabic Diacritization with Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/aclbulk-2015-belinkov-arabic-diacritization-recurrent-neural-networks/)
- [next → ASTD: Arabic Sentiment Tweets Dataset](https://md.research.elamur.ai/free/en/papers/nabil-2015-astd-arabic-sentiment-tweets-dataset/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/)

## References

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