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..
Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities
Authors: Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabás Póczos6892-6899
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to missing or perturbed modalities. ... it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICTMMMO, and You Tube. |
| Researcher Affiliation | Academia | School of Computer Science, Carnegie Mellon University EMAIL |
| Pseudocode | No | No pseudocode or algorithm block was found. |
| Open Source Code | Yes | 1Our source code is released at https://github.com/hainow/ MCTN. |
| Open Datasets | Yes | We use the CMU Multimodal Opinion-level Sentiment Intensity dataset (CMU-MOSI) which contains 2199 video segments... We also run experiments on ICT-MMMO (W ollmer et al. 2013) and You Tube (Morency, Mihalcea, and Doshi 2011) which consist of online review videos annotated for sentiment. |
| Dataset Splits | Yes | To be consistent with prior work, we use 52 segments for training, 10 for validation and 31 for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) were mentioned for running experiments. |
| Software Dependencies | No | Mentions GloVe word embeddings, Facet, COVAREP features, and P2FA for alignment, but no specific version numbers for any software dependencies are provided. |
| Experiment Setup | No | No specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations are explicitly provided in the main text. |