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Found 12 papers, 6 papers with code
Less is Better: A cognitively inspired unsupervised model for language segmentation
Language users process utterances by segmenting them into many cognitive units, which vary in their sizes and linguistic levels.
Modelling word learning and recognition using visually grounded speech
We furthermore investigate whether vector quantisation, a technique for discrete representation learning, aids the model in the discovery and recognition of words.
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge
In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning.
Semantic sentence similarity: size does not always matter
This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge.
Human Sentence Processing: Recurrence or Attention?
Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing.
Language learning using Speech to Image retrieval
Humans learn language by interaction with their environment and listening to other humans.
Simulating Spanish-English Code-Switching: El Modelo Est\'a Generating Code-Switches
To our knowledge, this is the first computational cognitive model that aims to simulate code-switched sentence production.
Dependency Parsing with your Eyes: Dependency Structure Predicts Eye Regressions During Reading
Backward saccades during reading have been hypothesized to be involved in structural reanalysis, or to be related to the level of text difficulty.
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)
Opinionated Natural Language Generation (ONLG) is a new, challenging, task that aims to automatically generate human-like, subjective, responses to opinionated articles online.
