Intent Detection is a vital component of any task-oriented conversational system. In order to understand the user’s current goal, the system must leverage its intent detector to classify the user’s utterance (provided in varied natural language) into one of several predefined classes, that is, intents.
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One of the keys to enable chatbots to communicate with human in a more natural way is the ability to handle long and complex user's utterances.
In this paper we present DELTA, a deep learning based language technology platform.
Ranked #3 on Text Classification on Yahoo! Answers
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 weights.
Ranked #6 on Intent Detection on SNIPS
Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition.
Ranked #3 on Intent Detection on ATIS
The underlying difference of linguistic patterns between general text and task-oriented dialogue makes existing pre-trained language models less useful in practice.
Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.
Ranked #3 on Slot Filling on ATIS
In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.
Ranked #1 on Intent Detection on SNIPS