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Found 6 papers, 1 papers with code
Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems
As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feature and target language.
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems
Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling.
Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances
In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives.
Bootstrapping NLU Models with Multi-task Learning
A common approach that is adapted in digital assistants when responding to a user query is to process the input in a pipeline manner where the first task is to predict the domain, followed by the inference of intent and slots.
A Morphology-aware Network for Morphological Disambiguation
In this paper, we propose a system that uses deep learning techniques for morphological disambiguation.
Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers
Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia.
