---
title: "Recurrent Continuous Translation Models — Kalchbrenner 2013"
description: "Phrase-based statistical machine translation treats phrase pairs with different surface forms as unrelated."
authors: ["Nal Kalchbrenner", "Phil Blunsom"]
year: 2013
venue: "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D13-1176/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/kalchbrenner-2013-recurrent-continuous-translation-models/"
---
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# Recurrent Continuous Translation Models

**Authors:** [Nal Kalchbrenner](https://md.research.elamur.ai/free/en/authors/nal-kalchbrenner/), [Phil Blunsom](https://md.research.elamur.ai/free/en/authors/phil-blunsom/) · **Year:** 2013 · **Venue:** Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D13-1176/) · [PDF](https://aclanthology.org/D13-1176.pdf) · [OpenAlex](https://openalex.org/W1753482797)

**Citations:** 1232 citations (openalex-snapshot · 2026-10-14) · [365](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Phrase-based statistical machine translation treats phrase pairs with different surface forms as unrelated. It also depends on latent alignments. Together these cause sparsity and weak generalisation to rare or unseen phrases. Kalchbrenner and Blunsom propose Recurrent Continuous Translation Models (RCTM), which work only with continuous vectors for words, phrases and sentences and use no alignments or phrase tables. A recurrent language model generates the target sentence word by word with no Markov truncation. It is conditioned on the source sentence through a Convolutional Sentence Model (CSM):

- **RCTM I** adds a single source-sentence vector to every hidden state.
- **RCTM II** uses a truncated CSM to produce intermediate representations aimed at target words.

The whole system is trained jointly. On English→French, the models reach a perplexity more than 43% below a strong IBM Model 2 variant. They react strongly to scrambled source word order. When reranking n-best lists, they nearly match the cdec system. This matters because it shows a single end-to-end neural model can learn both translation and target-language modelling without hand-built alignment machinery.

## Key points
- **Factorisation:** P(f|e) is broken into per-word conditionals, each depending on all earlier target words and the full source sentence. Scoring cost grows linearly with source and target length.
- **Perplexity:** the RCTMs beat IBM Model 1 by a wide margin and score more than 43% below a state-of-the-art IBM Model 2 variant (Dyer et al., 2013).
- **Word-order test:** randomly permuting source words makes perplexity against the references clearly worse, so the model depends on word position.
- **Sampling test:** RCTM II drew 2000 samples per sentence from its own distribution (French vocabulary of 34,831 words, top-5 restricted sampling). The outputs mostly agree with the source in number, tense and meaning.
- **Rescoring results:** the task was reranking cdec 1000-best lists on four WMT-NT sets.
  - RCTM + word penalty scored 19.7–19.8 / 21.1 / 22.5 / 21.5–21.7 Bleu.
  - The full cdec system with 12 features scored 19.9 / 21.2 / 22.6 / 21.8.
  - Adding a separate monolingual RLM gave no gain.

## Interesting ideas
- **Parser-free hierarchical encoding:** the convolutional encoder combines local n-grams first and then works up to the whole sentence, with no parse tree. This suits languages without good parsers, such as many low-resource ones, and the output never depends on a choice of parse.
- **Order-shuffling as a diagnostic:** comparing perplexity on original and shuffled source input is a cheap way to check whether a conditional model actually uses word order, and it applies to any sequence encoder.
- **Inspecting a model by sampling:** drawing candidates from a restricted top-k distribution and ranking them by model probability needs no external decoder. It is a lightweight way to see what a generative model has learned.
- **Minimal-feature reranking test:** pairing the model's log-probability with only a word penalty, then comparing against a richly engineered system, shows whether one learned score can stand in for many hand-made features.
- **Translation models as conditioned language models:** treating translation as a language model conditioned on a source vector is a flexible design. The authors note it could take extra context, such as discourse or multiple source languages, or use character-level recurrence for morphologically rich languages like Ukrainian.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Cited by

Cited by

365 papers of our library cite it (of the 140884 whose bibliographies we read); 61 of them have a page here, 304 not yet.

- [Towards String-To-Tree Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/aharoni-2017-string-tree-neural-machine-translation/)
- [Massively Multilingual Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/aharoni-2019-massively-multilingual-neural-machine-translation/)
- [Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/)
- [Incorporating Discrete Translation Lexicons into Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/arthur-2016-incorporating-discrete-translation-lexicons-into/)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
- [Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/bastings-2017-graph-convolutional-encoders-syntax-aware/)
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- [Massive Exploration of Neural Machine Translation Architectures](https://md.research.elamur.ai/free/en/papers/britz-2017-massive-exploration-neural-machine-translation/)
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- [Fast and Robust Neural Network Joint Models for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/devlin-2014-fast-robust-neural-network-joint/)
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- [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/gal-2015-theoretically-grounded-application-dropout-recurrent/)
- [A Convolutional Encoder Model for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/gehring-2017-convolutional-encoder-model-neural-machine/)
- [On Using Monolingual Corpora in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/gulcehre-2015-using-monolingual-corpora-neural-machine/)
- [Generating Sentences by Editing Prototypes](https://md.research.elamur.ai/free/en/papers/guu-2017-generating-sentences-editing-prototypes/)
- [Attention-based Multimodal Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/huang-2016-attention-based-multimodal-neural-machine/)
- [A Challenge Set Approach to Evaluating Machine Translation](https://md.research.elamur.ai/free/en/papers/isabelle-2017-challenge-set-approach-evaluating-machine/)
- [On Using Very Large Target Vocabulary for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/jean-2015-very-large-target-vocabulary-neural/)
- [A Convolutional Neural Network for Modelling Sentences](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2014-convolutional-neural-network-modelling-sentences/)
- [Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation](https://md.research.elamur.ai/free/en/papers/kasai-2020-deep-encoder-shallow-decoder-reevaluating/)
- [On the Impact of Various Types of Noise on Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/khayrallah-2018-impact-various-types-noise-neural/)
- [Controlling Output Length in Neural Encoder-Decoders](https://md.research.elamur.ai/free/en/papers/kikuchi-2016-controlling-output-length-neural-encoder/)
- [Scheduled Multi-Task Learning: From Syntax to Translation](https://md.research.elamur.ai/free/en/papers/kiperwasser-2018-scheduled-multi-task-learning-syntax/)
- [Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
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- [Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://md.research.elamur.ai/free/en/papers/laubli-2018-has-machine-translation-achieved-human/)
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## Cite

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

```bibtex
@inproceedings{kalchbrenner2013recurrent,
  title = {{Recurrent Continuous Translation Models}},
  author = {Kalchbrenner, Nal and Blunsom, Phil},
  booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
  year = {2013},
  doi = {10.18653/v1/d13-1176},
  url = {https://doi.org/10.18653/v1/d13-1176}
}
```

- [← previous Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue](https://md.research.elamur.ai/free/en/papers/lukin-2013-really-well-apparently-bootstrapping-improves/)
- [next → Recurrent Convolutional Neural Networks for Discourse Compositionality](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2013-recurrent-convolutional-neural-networks-discourse/)
- [↑ back to papers of 2013](https://md.research.elamur.ai/free/en/papers/2013/)

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