Recent advances in deep learning based dialogue systems: A systematic survey
Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …
real-life applications. It is also a complicated task since many NLP tasks deserving study are …
Neural approaches to conversational AI
This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …
few years. We group conversational systems into three categories:(1) question answering …
Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network
In this paper, we explore the slot tagging with only a few labeled support sentences (aka few-
shot). Few-shot slot tagging faces a unique challenge compared to the other few-shot …
shot). Few-shot slot tagging faces a unique challenge compared to the other few-shot …
Recent neural methods on slot filling and intent classification for task-oriented dialogue systems: A survey
In recent years, fostered by deep learning technologies and by the high demand for
conversational AI, various approaches have been proposed that address the capacity to …
conversational AI, various approaches have been proposed that address the capacity to …
Low-resource domain adaptation for compositional task-oriented semantic parsing
Task-oriented semantic parsing is a critical component of virtual assistants, which is
responsible for understanding the user's intents (set reminder, play music, etc.). Recent …
responsible for understanding the user's intents (set reminder, play music, etc.). Recent …
Coach: A coarse-to-fine approach for cross-domain slot filling
As an essential task in task-oriented dialog systems, slot filling requires extensive training
data in a certain domain. However, such data are not always available. Hence, cross …
data in a certain domain. However, such data are not always available. Hence, cross …
Robust zero-shot cross-domain slot filling with example values
Task-oriented dialog systems increasingly rely on deep learning-based slot filling models,
usually needing extensive labeled training data for target domains. Often, however, little to …
usually needing extensive labeled training data for target domains. Often, however, little to …
Augmented natural language for generative sequence labeling
We propose a generative framework for joint sequence labeling and sentence-level
classification. Our model performs multiple sequence labeling tasks at once using a single …
classification. Our model performs multiple sequence labeling tasks at once using a single …
How does the combined risk affect the performance of unsupervised domain adaptation approaches?
Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples
from the source domain and unlabeled samples from the target domain. Classical UDA …
from the source domain and unlabeled samples from the target domain. Classical UDA …
Generative zero-shot prompt learning for cross-domain slot filling with inverse prompting
Zero-shot cross-domain slot filling aims to transfer knowledge from the labeled source
domain to the unlabeled target domain. Existing models either encode slot descriptions and …
domain to the unlabeled target domain. Existing models either encode slot descriptions and …