Abstract Landgrebe and Smith (Synthese 198(March):2061–2081, 2021) present an unflattering diagnosis of recent advances in what they call language-centric artificial intelligence—perhaps more widely known as natural language processing: The models that are currently employed do not have sufficient expressivity, will generalize, fundamentally unable to induce linguistic semantics, say. is mainly...

Abstract We present a language model that combines large parametric neural network (i.e., transformer) with non-parametric episodic memory component in an integrated architecture. Our uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term retrieving set of nearest neighbor tokens at each timestep. design gating function adaptively combine ...

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, computer science. This stems from its high degree of subjectivity limited input sources that can effectively capture the actual sentiment. be even more with only text-based input. Meanwhile, rise deep learning an unprecedented large volume data have paved way for artifi...

Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs natural language (NLP) models have shown significant development, especially in text mining analysis. For vector-space text, there are famous like Word2vec, GloVe, fastText. In fact, NLP took a big step forward when BERT recently GTP-3 came out. this pape...