Question: What is improved by their proposed approach significantly?
Evidence: Domain classification is a task that predicts the most relevant domain given an input utterance BIBREF0. It is becoming more challenging since recent conversational interaction systems such as Amazon Alexa, Google Assistant, and Microsoft Cortana support more than thousands of domains developed by external developers BIBREF3, BIBREF2, BIBREF4. As they are independently and rapidly developed without a centralized ontology, multiple domains have overlapped capabilities that can process the same utterances. For example, “make an elephant sound” can be processed by AnimalSounds, AnimalNoises, and ZooKeeper domains.
Since there are a large number of domains, which are even frequently added or removed, it is infeasible to obtain all the ground-truth domains of the training utterances, and domain classifiers for conversational interaction systems are usually trained given only a small number (usually one) of ground-truths in the training utterances. This setting corresponds to multi-label positive and unlabeled (PU) learning, where assigned labels are positive, unassigned labels are not necessarily negative, and one or more labels are assigned for an instance BIBREF5, BIBREF6.
In this paper, we utilize user log data, which contain triples of an utterance, the predicted domain, and the response, for the model training. Therefore, we are given only one ground-truth for each training utterance. In order to improve the classification performance in this setting, if certain domains are repeatedly predicted with the highest confidences even though they are not the ground-truths of an utterance, we regard the domains as additional pseudo labels. This is closely related to pseudo labeling BIBREF7 or self-training BIBREF8, BIBREF9, BIBREF10. While the conventional pseudo labeling is used to derive target labels for unlabeled data, our approach adds pseudo labels to singly labeled data so that the data can have multiple target labels. Also, the approach is related to self-distillation, which leverages the confidence scores of the non-target outputs to improve the model performance BIBREF11, BIBREF12. While distillation methods utilize the confidence scores as the soft targets, pseudo labeling regards high confident outputs as the hard targets to further boost their confidences. We use both pseudo labeling and self-distillation in our work.
Pseudo labels can be wrongly derived when irrelevant domains are top predicted, which can lead the model training with wrong supervision. To mitigate this issue, we leverage utterances with negative system responses to lower the prediction confidences of the failing domains. For example, if a system response of a domain for an input utterance is “I don't know that one”, the domain is regarded as a negative ground-truth since it fails to handle the utterance.
Evaluating on an annotated dataset from the user logs of a large-scale conversation interaction system, we show that the proposed approach significantly improves the domain classification especially when hypothesis reranking is used BIBREF13, BIBREF4.
Evaluating on an annotated dataset from the user logs of a large-scale conversation interaction system, we show that the proposed approach significantly improves the domain classification especially when hypothesis reranking is used [14, 5].
Answer: The domain classification.