Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Browse subjects
  3. Computer Science
  4. Artificial Intelligence and Natural Language Processing
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

3241 results in Artificial Intelligence and Natural Language Processing

Page 29 of 163

  • NLE volume 27 issue 5 Cover and Back matter

  • Journal:
    Natural Language Engineering / Volume 27 / Issue 5 / September 2021
    Published online by Cambridge University Press:
    01 September 2021, pp. b1-b2
    • Article
      • You have access
    • PDF
    • Export citation

  • Emerging trends: Deep nets for poets

  • Kenneth Ward Church, Xiaopeng Yuan, Sheng Guo, Zewu Wu, Yehua Yang, Zeyu Chen
  • Journal:
    Natural Language Engineering / Volume 27 / Issue 5 / September 2021
    Published online by Cambridge University Press:
    01 September 2021, pp. 631-645
    • Article
      • You have access
      • Open access
    • PDF
    • HTML
    • Export citation

  • Deep nets have done well with early adopters, but the future will soon depend on crossing the chasm. The goal of this paper is to make deep nets more accessible to a broader audience including people with little or no programming skills, and people with little interest in training new models. A github is provided with simple implementations of image classification, optical character recognition, sentiment analysis, named entity recognition, question answering (QA/SQuAD), machine translation, speech to text (SST), and speech recognition (STT). The emphasis is on instant gratification. Non-programmers should be able to install these programs and use them in 15 minutes or less (per program). Programs are short (10–100 lines each) and readable by users with modest programming skills. Much of the complexity is hidden behind abstractions such as pipelines and auto classes, and pretrained models and datasets provided by hubs: PaddleHub, PaddleNLP, HuggingFaceHub, and Fairseq. Hubs have different priorities than research. Research is training models from corpora and fine-tuning them for tasks. Users are already overwhelmed with an embarrassment of riches (13k models and 1k datasets). Do they want more? We believe the broader market is more interested in inference (how to run pretrained models on novel inputs) and less interested in training (how to create even more models).

  • NLE volume 27 issue 5 Cover and Front matter

  • Journal:
    Natural Language Engineering / Volume 27 / Issue 5 / September 2021
    Published online by Cambridge University Press:
    01 September 2021, pp. f1-f2
    • Article
      • You have access
    • PDF
    • Export citation

Page 29 of 163