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..
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
Authors: Biao Zhang, Deyi Xiong, Jinsong Su
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the effectiveness of Batt RAE, we incorporate this semantic similarity as an additional feature into a state-of-the-art SMT system. Extensive experiments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.63 BLEU points on average over the baseline. |
| Researcher Affiliation | Academia | Xiamen University, Xiamen, China 3610051 Soochow University, Suzhou, China 2150062 |
| Pseudocode | No | The paper describes algorithms and procedures in narrative text and mathematical equations but does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at https://github.com/Deep Learn XMU/Batt RAE. |
| Open Datasets | Yes | Our parallel corpus consists of 1.25M sentence pairs extracted from LDC corpora6, with 27.9M Chinese words and 34.5M English words respectively. We trained a 5-gram language model on the Xinhua portion of the GIGAWORD corpus (247.6M English words) using SRILM Toolkit7 with modified Kneser-Ney Smoothing. |
| Dataset Splits | Yes | We used the NIST MT05 data set as the development set, and the NIST MT06/MT08 datasets as the test sets. [...] From these pairs, we further extracted 34K bilingual phrases as our development data to optimize all hyper-parameters using random search (Bergstra and Bengio 2012). |
| Hardware Specification | No | The paper does not specify any hardware details like CPU models, GPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions using 'SRILM Toolkit', 'Word2Vec', and 'lib LBFGS' but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | Finally, we set ds=dt=da=dsem=50, α=0.125 (such that, β=0.875), λL=1e 5, λrec=λatt=1e 4 and λsem=1e 3 according to experiments on the development data. Additionally, we set the maximum number of iterations in the L-BFGS algorithm to 100. |