Name
Abstract | ||
|---|---|---|
Building models for natural language processing (NLP) tasks remains a daunting task for many, requiring significant technical expertise, efforts, and resources. In this demonstration, we present AutoText, an end-to-end AutoAI framework for text, to lower the barrier of entry in building NLP models. AutoText combines state-of-the-art AutoAI optimization techniques and learning algorithms for NLP tasks into a single extensible framework. Through its simple, yet powerful UI, non-AI experts (e.g., domain experts) can quickly generate performant NLP models with support to both control (e.g., via specifying constraints) and understand learned models. |
Year | Venue | Keywords |
|---|---|---|
2021 | THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Task (project management),Domain (software engineering),Software engineering,Computer science,End-to-end principle,Control (linguistics),Extensibility |
DocType | Volume | ISSN |
Conference | 35 | 2159-5399 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
15 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arunima Chaudhary | 1 | 0 | 0.34 |
| Alayt Issak | 2 | 0 | 0.34 |
| Kiran Kate | 3 | 0 | 0.34 |
| Yannis Katsis | 4 | 15 | 3.12 |
| Abel N. Valente | 5 | 0 | 0.68 |
| Dakuo Wang | 6 | 73 | 14.74 |
| Alexandre V. Evfimievski | 7 | 501 | 41.76 |
| Sairam Gurajada | 8 | 118 | 7.83 |
| Ban Kawas | 9 | 30 | 5.22 |
| A Cristiano I Malossi | 10 | 65 | 9.29 |
| Ling-ling Yan | 11 | 1273 | 70.78 |
| Tejaswini Pedapati | 12 | 16 | 1.25 |
| Horst Samulowitz | 13 | 316 | 26.05 |
| Martin Wistuba | 14 | 154 | 19.66 |
| Yunyao Li | 15 | 530 | 37.81 |