Spoken Language Understanding

106 papers with code • 5 benchmarks • 13 datasets

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Use these libraries to find Spoken Language Understanding models and implementations

Most implemented papers

Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

snipsco/snips-nlu 25 May 2018

This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.

SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering

Microsoft/SDNet 10 Dec 2018

Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.

SpeechBrain: A General-Purpose Speech Toolkit

speechbrain/speechbrain 8 Jun 2021

SpeechBrain is an open-source and all-in-one speech toolkit.

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

MiuLab/SlotGated-SLU NAACL 2018

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.

Spoken Language Understanding on the Edge

sonos/spoken-language-understanding-research-datasets 30 Oct 2018

We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications.

A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

LeePleased/StackPropagation-SLU IJCNLP 2019

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.

CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding

Adaxry/CM-Net IJCNLP 2019

Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works.

Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models

lorenlugosch/end-to-end-SLU 21 Oct 2019

End-to-end models are an attractive new approach to spoken language understanding (SLU) in which the meaning of an utterance is inferred directly from the raw audio without employing the standard pipeline composed of a separately trained speech recognizer and natural language understanding module.