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..
Translations as Additional Contexts for Sentence Classification
Authors: Reinald Kim Amplayo, Kyungjae Lee, Jinyoung Yeo, Seung-won Hwang
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our method performs competitively compared to previous models, achieving best classification performance on multiple data sets. |
| Researcher Affiliation | Academia | Yonsei University, Seoul, South Korea Pohang University of Science and Technology, Pohang, South Korea |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code we use in this paper is publicly shared: https: //github.com/rktamplayo/MCFA |
| Open Datasets | Yes | (a) MR4 [Pang and Lee, 2005]: Movie reviews data... (b) SUBJ [Pang and Lee, 2004]: Subjectivity data... (c) CR5 [Hu and Liu, 2004]: Customer reviews... (d) TREC6 [Li and Roth, 2002]: TREC question data set... |
| Dataset Splits | Yes | If not, we use 10-fold cross validation (marked as CV) with random split. ... We perform early stopping using a random 10% of the training set as the development set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'polyglot library' and 'Fast Text pre-trained vectors' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For our CNN, we use rectified linear units and three filters with different window sizes h = 3, 4, 5 with 100 feature maps each... We use dropout... with a dropout rate of 0.5. We also use an l2 constraint of 3... During training, we use mini-batch size of 50. Training is done via stochastic gradient descent over shuffled mini-batches with the Adadelta update rule. |