NTR organizes and hosts scientific webinars on neural networks
and invites speakers from all over the world to present their recent
work.
On March 29 Aleksandr Chuklin, Google, Zürich, Switzerland, presented a technical Zoom webinar on Text Generation with Text-Editing Models.
About the webinar:
In this talk we provided an overview of the text edit based models and current state-of-the-art approaches analyzing their pros and cons.
Text editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, text simplification, and style transfer.
These tasks share a common trait — they exhibit a large amount of textual overlap between the source and target texts. Text editing models take advantage of this observation and learn to generate the output by predicting edit operations applied to the source sequence.
Such models provide several benefits over seq2seq models, including faster inference speed, higher sample efficiency, and better control and interpretability of the outputs.
Moderator and contact: NTR CEO Nick Mikhailovsky: nickm@ntrlab.com.