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Found 9 papers, 2 papers with code
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning
As the top-level intent largely governs the syntax and semantics of a parse, the intent conditioning allows the model to better control beam search and improves the quality and diversity of top-k outputs.
Overcoming Conflicting Data when Updating a Neural Semantic Parser
In this paper, we explore how to use a small amount of new data to update a task-oriented semantic parsing model when the desired output for some examples has changed.
Update Frequently, Update Fast: Retraining Semantic Parsing Systems in a Fraction of Time
To reduce training time, one can fine-tune the previously trained model on each patch, but naive fine-tuning exhibits catastrophic forgetting - degradation of the model performance on the data not represented in the data patch.
Improving Robustness of Task Oriented Dialog Systems
However, evaluating a model's robustness to these changes is harder for language since words are discrete and an automated change (e. g. adding `noise') to a query sometimes changes the meaning and thus labels of a query.
Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog
We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes.
Improving Semantic Parsing for Task Oriented Dialog
Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results [Gupta et al 2018].
PyText: A Seamless Path from NLP research to production
We introduce PyText - a deep learning based NLP modeling framework built on PyTorch.
Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog
We use this data set to evaluate three different cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations.
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations
Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots.
