Simplifying and/or paraphrasing complex textual content by jointly learning semantic alignment and simplicity
US11042712B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 4, 2019 |
| Grant date | Jun 22, 2021 |
| Priority date | — |
| Expiry date | Sep 18, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are described herein for training machine learning models to simplify (e.g., paraphrase) complex textual content by ensuring that the machine learning models jointly learn both semantic alignment and notions of simplicity. In various embodiments, an input textual segment having multiple tokens and being associated with a first measure of simplicity may be applied as input across a trained machine learning model to generate an output textual segment. The output textual segment may be is semantically aligned with the input textual segment and associated with a second measure of simplicity that is greater than the first measure of simplicity (e.g., a paraphrase thereof). The trained machine learning model may include an encoder portion and a decoder portion, as well as control layer(s) trained to maximize the second measure of simplicity by replacing token(s) of the input textual segment with replacement token(s).
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.