# Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction

arXiv: https://arxiv.org/abs/2212.10728 · HTML: https://mlsanity.com/paper/2212.10728/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1907.00390/) (2019)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding](https://mlsanity.com/paper/1909.06937/) (2019)
- [Learning to Classify Intents and Slot Labels Given a Handful of Examples](https://mlsanity.com/paper/2004.10793/) (2020)
- [Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model](https://mlsanity.com/paper/1907.02884/) (2019)
- [CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection](https://mlsanity.com/paper/2106.06213/) (2021)
- [Multi-Task Networks With Universe, Group, and Task Feature Learning](https://mlsanity.com/paper/1907.01791/) (2019)
- [Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances](https://mlsanity.com/paper/1904.10500/) (2019)
- [Bi-directional Joint Neural Networks for Intent Classification and Slot Filling](https://mlsanity.com/paper/2202.13079/) (2022)
- [Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks](https://mlsanity.com/paper/1709.10191/) (2017)
- [Parallel Intent and Slot Prediction using MLB Fusion](https://mlsanity.com/paper/2003.09211/) (2020)
- [Cross-lingual transfer learning for spoken language understanding](https://mlsanity.com/paper/1904.01825/) (2019)

## Abstract (arXiv)

When a human communicates with a machine using natural language on the web and online, how can it understand the human's intention and semantic context of their talk? This is an important AI task as it enables the machine to construct a sensible answer or perform a useful action for the human. Meaning is represented at the sentence level, identification of which is known as intent detection, and at the word level, a labelling task called slot filling. This dual-level joint task requires innovative thinking about natural language and deep learning network design, and as a result, many approaches and models have been proposed and applied. This tutorial will discuss how the joint task is set up and introduce Spoken Language Understanding/Natural Language Understanding (SLU/NLU) with Deep Learning techniques. We will cover the datasets, experiments and metrics used in the field. We will describe how the machine uses the latest NLP and Deep Learning techniques to address the joint task, including recurrent and attention-based Transformer networks and pre-trained models (e.g. BERT). We will then look in detail at a network that allows the two levels of the task, intent classification and slot filling, to interact to boost performance explicitly. We will do a code demonstration of a Python notebook for this model and attendees will have an opportunity to watch coding demo tasks on this joint NLU to further their understanding.