Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neural Machine Translation by Jointly Learning to Align and Translate
Authors: Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio
ICLR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed approach on the task of English-to-French translation. We use the bilingual, parallel corpora provided by ACL WMT 14. As a comparison, we also report the performance of an RNN Encoder Decoder which was proposed recently by Cho et al. (2014a). |
| Researcher Affiliation | Academia | Dzmitry Bahdanau Jacobs University Bremen, Germany Kyung Hyun Cho Yoshua Bengio Universit e de Montr eal |
| Pseudocode | No | The paper describes the model architecture and training procedure using mathematical equations and descriptive text, but no explicit 'Pseudocode' or 'Algorithm' block is provided. |
| Open Source Code | Yes | Implementations are available at https://github.com/lisa-groundhog/Ground Hog. |
| Open Datasets | Yes | We use the bilingual, parallel corpora provided by ACL WMT 14.3 http://www.statmt.org/wmt14/translation-task.html |
| Dataset Splits | Yes | We concatenate news-test-2012 and news-test-2013 to make a development (validation) set, and evaluate the models on the test set (news-test-2014) from WMT 14, which consists of 3003 sentences not present in the training data. |
| Hardware Specification | Yes | TITAN BLACK, Quadro K-6000 (from Table 2) |
| Software Dependencies | No | The paper mentions software like Theano, Adadelta, and Moses for tokenization, but specific version numbers for these dependencies are not provided. |
| Experiment Setup | Yes | The encoder and decoder of the RNNencdec have 1000 hidden units each. ... We use a minibatch stochastic gradient descent (SGD) algorithm together with Adadelta (Zeiler, 2012) to train each model. Each SGD update direction is computed using a minibatch of 80 sentences. (Further details in Appendix A.2.3 and B.2 regarding hidden units (1000), embedding dimensionality (620), maxout layer size (500), Adadelta parameters (ϵ = 10^-6 and ρ = 0.95), and gradient normalization threshold (1)). |