Papers by Andrzej Cichocki

2 papers
Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint (2021.findings-acl)

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Challenge: despite the diversity of linguistic structures, vector embedding models lack order-preserving properties . current methods for learning linguistic structure can be expensive and time-consuming .
Approach: They propose a method for embedding documents and words in rotation group . they capture word order and higher-order word interactions .
Outcome: The proposed model achieves the best results in document classification benchmarks.
CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network (2021.acl-long)

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Challenge: Existing methods for multimodal sentiment analysis require all modalities as input, thus are sensitive to missing modality at predicting time.
Approach: They propose to model bi-direction interplay via couple learning and exploit multiple bi-directional translations to exploit multimodal fusion embeddings.
Outcome: The proposed framework achieves state-of-the-art or often competitive performance on two multimodal benchmarks with extensive ablation studies.

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