Pattern recognition with rejection: Application to handwritten digits

Homenda, Władysław; Luckner, Marcin

doi:10.1109/wict.2014.7077288

Cited by 1 publication

(1 citation statement)

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“…Traditional classification can be implemented, for example, by training a specific OOS class to set up a rejection threshold, or even by training a binary classifier (Larson et al, 2019). Given that no specific domain information or structure are taken into account, those methods are roughly the same that have been previously applied for other classification problems (Fumera et al, 2003;Luckner and Homenda, 2014). Some recent effort has been put specifically for OOS sample detection for intent recognition, either by considering OOS data during the training process (Tan et al, 2019) or solely by improving in-scope sample representation by means of Auto Encoders (Ryu et al, 2017) and Generative Adversarial Neural Networks (GANs) (Ryu et al, 2018), which is more desirable since there is no reliance on tedious data gathering processes to represent unpredictable OOS inputs.…”

Section: Related Workmentioning

confidence: 99%

Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes

Cavalin¹,

Ribeiro²,

Appel³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques. The approach, inspired by a previous algorithm used for an inverse dictionary task, allows the classification algorithm to take in account inter-class similarities provided by the repeated occurrence of some words in the training examples of the different classes. The classification is carried out by mapping text embeddings to the word graph embeddings of the classes. Focusing solely on improving the representation of the class label set, we show in experiments conducted in both private and public intent classification datasets, that better detection of out-of-scope examples (OOS) is achieved and, as a consequence, that the overall accuracy of intent classification is also improved. In particular, using the recently-released Larson dataset, an error of about 9.9% has been achieved for OOS detection, beating the previous state-of-the-art result by more than 31 percentage points.

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