Multimodal intelligence: Representation learning, information fusion, and applications
Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …
natural language processing since 2010. Each of these tasks involves a single modality in …
[HTML][HTML] Semantic memory: A review of methods, models, and current challenges
AA Kumar - Psychonomic Bulletin & Review, 2021 - Springer
Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …
system that consists of knowledge about the world, concepts, and symbols. Considerable …
[HTML][HTML] Multimodal transformer for unaligned multimodal language sequences
Human language is often multimodal, which comprehends a mixture of natural language,
facial gestures, and acoustic behaviors. However, two major challenges in modeling such …
facial gestures, and acoustic behaviors. However, two major challenges in modeling such …
Deep multimodal representation learning: A survey
W Guo, J Wang, S Wang - Ieee Access, 2019 - ieeexplore.ieee.org
Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …
What artificial neural networks can tell us about human language acquisition
A Warstadt, SR Bowman - Algebraic structures in natural …, 2022 - taylorfrancis.com
Rapid progress in machine learning for natural language processing has the potential to
transform debates about how humans learn language. However, the learning environments …
transform debates about how humans learn language. However, the learning environments …
The importance of modeling social factors of language: Theory and practice
Natural language processing (NLP) applications are now more powerful and ubiquitous
than ever before. With rapidly developing (neural) models and ever-more available data …
than ever before. With rapidly developing (neural) models and ever-more available data …
Found in translation: Learning robust joint representations by cyclic translations between modalities
Multimodal sentiment analysis is a core research area that studies speaker sentiment
expressed from the language, visual, and acoustic modalities. The central challenge in …
expressed from the language, visual, and acoustic modalities. The central challenge in …
Vector-space models of semantic representation from a cognitive perspective: A discussion of common misconceptions
Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …
A survey of cross-lingual word embedding models
Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when developing …
multilingual contexts and are a key facilitator of cross-lingual transfer when developing …
Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks
We hypothesize that due to the greedy nature of learning in multi-modal deep neural
networks, these models tend to rely on just one modality while under-fitting the other …
networks, these models tend to rely on just one modality while under-fitting the other …