Blessed are students of natural language processing. In order to track the progress of natural language processing (NLP), a large number of people with lofty ideals maintain a library called NLP-Progress on Github. It records the baseline and standard data sets of almost all NLP tasks, as well as the state-of-the-art of these issues.Github
NLP-Progress also covers traditional NLP tasks, such as dependency parsing and part-of-speech tagging, and some new tasks, such as reading comprehension and natural language reasoning. It not only provides readers with baselines and standard data sets for these tasks, but also records the state-of-the-art of these issues.
The following editor briefly lists several tasks recorded by NLP-Progress:Coreference resolution co-referential resolution
Domain Adaption domain migration
Language modeling language model
Machine translation Machine translation
Multi-task learning Multi-task learning
Multi- modal Multimodal
Named entity recognition
Natural language inference
Part-of-speech tagging Part-of-speech tagging
Semantic textual similarity Semantic textual similarity
Semantic role labeling
Taxonomy learning Taxonomy learning
Time series analysis Text classification
Word sense disambiguation
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For each task, NLP-Progress will briefly introduce what the task does, and list the public standard data set in detail, as well as the current ranking of each model on the data set. For example, the more popular Question answering question answering system task, its organization is as follows:
Specific to a certain open data set, such as Quasar, the contributor will briefly introduce the composition of the data set, and then list the paper rankings, each line of which includes: model, effect, article name and link, and code link.
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The original publication time is: 2018-11-15