# A Robust Semantic Frame Parsing Pipeline on a New Complex Twitter Dataset

arXiv: https://arxiv.org/abs/2212.08987 · HTML: https://mlsanity.com/paper/2212.08987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 18 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [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)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding](https://mlsanity.com/paper/1812.06083/) (2018)
- [Semantic Frame Parsing for Information Extraction : the CALOR corpus](https://mlsanity.com/paper/1812.08039/) (2018)
- [An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction](https://mlsanity.com/paper/2106.14611/) (2021)

## Abstract (arXiv)

Most recent semantic frame parsing systems for spoken language understanding (SLU) are designed based on recurrent neural networks. These systems display decent performance on benchmark SLU datasets such as ATIS or SNIPS, which contain short utterances with relatively simple patterns. However, the current semantic frame parsing models lack a mechanism to handle out-of-distribution (OOD) patterns and out-of-vocabulary (OOV) tokens. In this paper, we introduce a robust semantic frame parsing pipeline that can handle both OOD patterns and OOV tokens in conjunction with a new complex Twitter dataset that contains long tweets with more OOD patterns and OOV tokens. The new pipeline demonstrates much better results in comparison to state-of-the-art baseline SLU models on both the SNIPS dataset and the new Twitter dataset (Our new Twitter dataset can be downloaded from https://1drv.ms/u/s!AroHb-W6OAlavK4begsDsMALfE?e=c8f2XX ). Finally, we also build an E2E application to demo the feasibility of our algorithm and show why it is useful in real application.