High-performance intent classification in sparse supervised data conditions
Name
1128822757-MIT.pdf
Size
5.62 MB
Format
Adobe PDF
Checksum (MD5)
b52d2ebbf640158e31fe042d606fb092
Author(s)
Galli, Keith(Keith R.)
Advisor(s)
James Glass and Ken Leidal.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Intent recognition is the process of taking short messages, called utterances, and automatically classifying them as a specific intent from a set of possible intents. A model is trained using a number of sample utterances from each intent with the goal of being able to classify unseen utterances with high accuracy. The objective of this work is to build an intent recognition tool for chatbots that outperforms all other natural language understanding services available. In order to do this, we first created a corpus that could be used to test models in a wide variety of different conditions. Next we performed a comprehensive review of possible models by looking at relevant work in natural language processing and applying these techniques to intent recognition. Finally we present the Shallow Attention Extension that builds off of state-of-the-art language models to produce an intent recognition model with deep semantic understanding of English phrases. This model exhibits very high performance in a large set of different testing environments.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 59-62).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
