Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …

Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

Replacing static word embeddings with contextualized word representations has yielded
significant improvements on many NLP tasks. However, just how contextual are the …

Deep multimodal fusion by using multiple sources of data for classification or regression has
exhibited a clear advantage over the unimodal counterpart on various applications. Yet …

Language understanding research is held back by a failure to relate language to the
physical world it describes and to the social interactions it facilitates. Despite the incredible …

Abstract Machine learning about language can be improved by supplying it with specific
knowledge and sources of external information. We present here a new version of the linked …

Extensive evaluation on a large number of word embedding models for language
processing applications is conducted in this work. First, we introduce popular word …

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 …

Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …

We study the topmost weight matrix of neural network language models. We show that this
matrix constitutes a valid word embedding. When training language models, we recommend …