# Improved intent classification based on context information using a windows-based approach

arXiv: https://arxiv.org/abs/2411.06022 · HTML: https://mlsanity.com/paper/2411.06022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 9 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://mlsanity.com/paper/2004.06871/) (2020)
- [Benchmarking Natural Language Understanding Services for building Conversational Agents](https://mlsanity.com/paper/1903.05566/) (2019)
- [BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1907.03040/) (2019)
- [Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker](https://mlsanity.com/paper/2002.02450/) (2020)
- [Subword Semantic Hashing for Intent Classification on Small Datasets](https://mlsanity.com/paper/1810.07150/) (2018)

## Abstract (arXiv)

Conversational systems have a Natural Language Understanding (NLU) module. In this module, there is a task known as an intent classification that aims at identifying what a user is attempting to achieve from an utterance. Previous works use only the current utterance to predict the intent of a given query and they do not consider the role of the context (one or a few previous utterances) in the dialog flow for this task. In this work, we propose several approaches to investigate the role of contextual information for the intent classification task. Each approach is used to carry out a concatenation between the dialogue history and the current utterance. Our intent classification method is based on a convolutional neural network that obtains effective vector representations from BERT to perform accurate intent classification using an approach window-based. Our experiments were carried out on a real-world Brazilian Portuguese corpus with dialog flows provided by Wavy global company. Our results achieved substantial improvements over the baseline, isolated utterances (without context), in three approaches using the user's utterance and system's response from previous messages as dialogue context.