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..
Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation
Authors: Jinsong Su, Zhixing Tan, Deyi Xiong, Rongrong Ji, Xiaodong Shi, Yang Liu
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on Chinese-English translation demonstrate the superiorities of the proposed encoders over the conventional encoder. |
| Researcher Affiliation | Academia | Xiamen University, Xiamen, China1 Soochow University, Suzhou, China2 Tsinghua University, Beijing, China3 |
| Pseudocode | No | The paper presents mathematical equations for the GRU and its variants, along with architectural diagrams, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'the toolkit2 released by Stanford to train word segmenters' and provides a URL (http://nlp.stanford.edu/software/segmenter.html#Download). However, this refers to a third-party tool used by the authors, not the open-sourcing of their own proposed methodology's code. |
| Open Datasets | Yes | Our training data consists of 1.25M sentence pairs extracted from LDC2002E18, LDC2003E07, LDC2003E14, Hansards portion of LDC2004T07, LDC2004T08 and LDC2005T06, with 27.9M Chinese words and 34.5M English words. |
| Dataset Splits | Yes | We chosed the NIST 2005 dataset as the validation set and the NIST 2002, 2003, 2004, 2006, and 2008 datasets as test sets. |
| Hardware Specification | Yes | We used a single GPU device Titan X to train models. |
| Software Dependencies | No | The paper mentions 'Rmsprop (Graves 2013)' and 'multi-bleu.perl script', and 'the toolkit2 released by Stanford' but does not specify version numbers for any software components, libraries, or dependencies used for the experiments. |
| Experiment Setup | Yes | During this procedure, we set the following hyper-parameters: word embedding dimension as 320, hidden layer size as 512, learning rate as 5 10 4, batch size as 80, gradient norm as 1.0. |