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 | Conference PDF | Archive PDF | Plain Text | 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 {htpham,pliang,tmanzini,morency,bapoczos}@cs.cmu.edu |
| 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. |