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Found 11 papers, 0 papers with code
Compositional Generalization in Spoken Language Understanding
State-of-the-art spoken language understanding (SLU) models have shown tremendous success in benchmark SLU datasets, yet they still fail in many practical scenario due to the lack of model compositionality when trained on limited training data.
Contextual Data Augmentation for Task-Oriented Dialog Systems
While dialog response generation has been widely studied on the agent side, it is not evident if similar generative models can be used to generate a large variety of, and often unexpected, user inputs that real dialog systems encounter in practice.
Code-Switched Text Synthesis in Unseen Language Pairs
In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training data.
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU
This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection.
Generating Dialogue Responses from a Semantic Latent Space
Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary.
Fast Domain Adaptation of Semantic Parsers via Paraphrase Attention
However, state-of-the art attention based neural parsers are slow to retrain which inhibits real time domain adaptation.
Iterative Delexicalization for Improved Spoken Language Understanding
Recurrent neural network (RNN) based joint intent classification and slot tagging models have achieved tremendous success in recent years for building spoken language understanding and dialog systems.
SkillBot: Towards Automatic Skill Development via User Demonstration
We present SkillBot that takes the first step to enable end users to teach new skills in personal assistants (PA).
Robust Spoken Language Understanding via Paraphrasing
Learning intents and slot labels from user utterances is a fundamental step in all spoken language understanding (SLU) and dialog systems.
CRUISE: Cold-Start New Skill Development via Iterative Utterance Generation
We present a system, CRUISE, that guides ordinary software developers to build a high quality natural language understanding (NLU) engine from scratch.
The Search Problem in Mixture Models
We consider the task of learning the parameters of a {\em single} component of a mixture model, for the case when we are given {\em side information} about that component, we call this the "search problem" in mixture models.
