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 …

Intent classification, to identify the speaker's intention, and slot filling, to label each token
with a semantic type, are critical tasks in natural language understanding. Traditionally the …

Intent classification and slot filling are two essential tasks for natural language
understanding. They often suffer from small-scale human-labeled training data, resulting in …

Attention-based recurrent neural network models for joint intent detection and slot filling
have achieved the state-of-the-art performance, while they have independent attention …

Attention-based encoder-decoder neural network models have recently shown promising
results in machine translation and speech recognition. In this work, we propose an attention …

Being able to recognize words as slots and detect the intent of an utterance has been a keen
issue in natural language understanding. The existing works either treat slot filling and intent …

Semantic slot filling is one of the most challenging problems in spoken language
understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for …

We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for
Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M …

We present MCTest, a freely available set of stories and associated questions intended for
research on the machine comprehension of text. Previous work on machine comprehension …

Two major tasks in spoken language understanding (SLU) are intent determination (ID) and
slot filling (SF). Recurrent neural networks (RNNs) have been proved effective in SF, while …