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Paradigm Shift in Natural Language Processing
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2021
arXiv
pre-print
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For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, NER, Chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. ...
In the era of deep learning, modeling for most NLP tasks has converged to several mainstream paradigms. ...
Sentiment Classification (ALSC), Aspect-oriented Opinion Extraction (AOE), Aspect Term Extraction and Sentiment Classification (AESC), Pair Extraction (Pair), and Triplet Extraction (Triplet). ...
arXiv:2109.12575v1
fatcat:vckeva3u3va3vjr6okhuztox4y
Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning
2022
The Journal of Artificial Intelligence Research
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
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These methods combine generative language learning techniques with neural-networks based frameworks. ...
Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). ...
Aksenov et al. (2020) conditioned the encoder and decoder of a Transformer-based neural model on the BERT language models for English and German. ...
doi:10.1613/jair.1.12918
fatcat:xfnul3j5azchfe6pvgvwy3z6em