# Improve Text Classification Accuracy with Intent Information

arXiv: https://arxiv.org/abs/2212.07649 · HTML: https://mlsanity.com/paper/2212.07649/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 15 Dec 2022.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DynaBERT: Dynamic BERT with Adaptive Width and Depth](https://mlsanity.com/paper/2004.04037/) (2020)
- [SLURP: A Spoken Language Understanding Resource Package](https://mlsanity.com/paper/2011.13205/) (2020)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Joint Embedding of Words and Labels for Text Classification](https://mlsanity.com/paper/1805.04174/) (2018)
- [Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations](https://mlsanity.com/paper/1905.06139/) (2019)
- [Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation](https://mlsanity.com/paper/2201.08702/) (2022)
- [Learning ASR-Robust Contextualized Embeddings for Spoken Language Understanding](https://mlsanity.com/paper/1909.10861/) (2019)
- [Adapting Pretrained Transformer to Lattices for Spoken Language Understanding](https://mlsanity.com/paper/2011.00780/) (2020)
- [PIN: A Novel Parallel Interactive Network for Spoken Language Understanding](https://mlsanity.com/paper/2009.13431/) (2020)
- [Towards Joint Intent Detection and Slot Filling via Higher-order Attention](https://mlsanity.com/paper/2109.08890/) (2021)
- [Spoken Language Intent Detection using Confusion2Vec](https://mlsanity.com/paper/1904.03576/) (2019)
- [Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding](https://mlsanity.com/paper/2205.00693/) (2022)

## Abstract (arXiv)

Text classification, a core component of task-oriented dialogue systems, attracts continuous research from both the research and industry community, and has resulted in tremendous progress. However, existing method does not consider the use of label information, which may weaken the performance of text classification systems in some token-aware scenarios. To address the problem, in this paper, we introduce the use of label information as label embedding for the task of text classification and achieve remarkable performance on benchmark dataset.