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Found 13 papers, 0 papers with code
Temporal Generalization for Spoken Language Understanding
Spoken Language Understanding (SLU) models in industry applications are usually trained offline on historic data, but have to perform well on incoming user requests after deployment.
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding
This paper provides the first experimental study on the impact of using domain-specific representations on a BERT-based multi-task spoken language understanding (SLU) model for multi-domain applications.
Distributionally Robust Finetuning BERT for Covariate Drift in Spoken Language Understanding
Experiments show that a state-of-the-art BERT-based model suffers performance loss under this drift.
To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding?
This paper addresses the question as to what degree a BERT-based multilingual Spoken Language Understanding (SLU) model can transfer knowledge across languages.
Data balancing for boosting performance of low-frequency classes in Spoken Language Understanding
Despite the fact that data imbalance is becoming more and more common in real-world Spoken Language Understanding (SLU) applications, it has not been studied extensively in the literature.
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding
A typical cross-lingual transfer learning approach boosting model performance on a language is to pre-train the model on all available supervised data from another language.
Cross-lingual Transfer Learning for Japanese Named Entity Recognition
This work explores cross-lingual transfer learning (TL) for named entity recognition, focusing on bootstrapping Japanese from English.
Cross-lingual transfer learning for spoken language understanding
Typically, spoken language understanding (SLU) models are trained on annotated data which are costly to gather.
Neural Named Entity Recognition from Subword Units
Named entity recognition (NER) is a vital task in spoken language understanding, which aims to identify mentions of named entities in text e. g., from transcribed speech.
Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System
The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests.
