TEXT Open Intent Recognition (TEXTOIR)
TEXTOIR is the first high-quality Text Open Intent Recognition platform. This repo contains a convenient toolkit with extensible interfaces, integrating a series of state-of-the-art algorithms of two tasks (open intent detection and open intent discovery).
Introduction
TEXTOIR aims to provide a convenience toolkit for researchers to reproduce the related text open classification and clustering methods. It contains two tasks, which are defined as open intent detection and open intent discovery. Open intent detection aims to identify n-class known intents, and detect one-class open intent. Open intent discovery aims to leverage limited prior knowledge of known intents to find fine-grained known and open intent-wise clusters.
Benchmark Datasets： BANKING、OOS/CLINC150、StackOverflow.
Integrated Models
Open Intent Detection: OpenMax\MSP\DOC\DeepUnk\SEG\ADB\ (K+1)-way\MDF\ARPL\KNNCL\DA-ADB.
New Intent Discovery:
a.Unsupervised: KM\AG\SAE-KM\DEC\DCN\CC\SCCL\USNID.
b.Semi-supervised:
KCL\MCL\DTC*\CDAC+*\DeepAligned\GCD\MTP-CLNN\USNID.
( * denotes the CV model replaced with the BERT backbone)
MIT License
Copyright (c) 2022 THUIAR