Analysis methods in neural language processing: A survey
Y Belinkov, J Glass - … of the Association for Computational Linguistics, 2019 - direct.mit.edu
The field of natural language processing has seen impressive progress in recent years, with
neural network models replacing many of the traditional systems. A plethora of new models …
neural network models replacing many of the traditional systems. A plethora of new models …
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 …
Language is not all you need: Aligning perception with language models
A big convergence of language, multimodal perception, action, and world modeling is a key
step toward artificial general intelligence. In this work, we introduce KOSMOS-1, a …
step toward artificial general intelligence. In this work, we introduce KOSMOS-1, a …
Experience grounds language
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 …
physical world it describes and to the social interactions it facilitates. Despite the incredible …
Multimodal machine learning: A survey and taxonomy
T Baltrušaitis, C Ahuja… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
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 …
odors, and taste flavors. Modality refers to the way in which something happens or is …
Diachronic word embeddings reveal statistical laws of semantic change
Understanding how words change their meanings over time is key to models of language
and cultural evolution, but historical data on meaning is scarce, making theories hard to …
and cultural evolution, but historical data on meaning is scarce, making theories hard to …
Directional skip-gram: Explicitly distinguishing left and right context for word embeddings
In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of
the skip-gram model by explicitly distinguishing left and right context in word prediction. In …
the skip-gram model by explicitly distinguishing left and right context in word prediction. In …
Microsoft coco captions: Data collection and evaluation server
In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When
completed, the dataset will contain over one and a half million captions describing over …
completed, the dataset will contain over one and a half million captions describing over …
Improving distributional similarity with lessons learned from word embeddings
Recent trends suggest that neural-network-inspired word embedding models outperform
traditional count-based distributional models on word similarity and analogy detection tasks …
traditional count-based distributional models on word similarity and analogy detection tasks …
Neural word embedding as implicit matrix factorization
O Levy, Y Goldberg - Advances in neural information …, 2014 - proceedings.neurips.cc
We analyze skip-gram with negative-sampling (SGNS), a word embedding method
introduced by Mikolov et al., and show that it is implicitly factorizing a word-context matrix …
introduced by Mikolov et al., and show that it is implicitly factorizing a word-context matrix …