# Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification

arXiv: https://arxiv.org/abs/2010.05990 · HTML: https://mlsanity.com/paper/2010.05990/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions](https://mlsanity.com/paper/2403.07678/) (2024)
- [Improving Paraphrase Detection with the Adversarial Paraphrasing Task](https://mlsanity.com/paper/2106.07691/) (2021)
- [Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training](https://mlsanity.com/paper/2404.01157/) (2024)
- [Text-to-Text Multi-view Learning for Passage Re-ranking](https://mlsanity.com/paper/2104.14133/) (2021)
- [A Text-to-Text Model for Multilingual Offensive Language Identification](https://mlsanity.com/paper/2312.03379/) (2023)
- [Word Matters: What Influences Domain Adaptation in Summarization?](https://mlsanity.com/paper/2406.14828/) (2024)
- [Improving Customer Service Chatbots with Attention-based Transfer Learning](https://mlsanity.com/paper/2111.14621/) (2021)

## Abstract (arXiv)

In this work, we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of deep learning chatbots for task classification. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transformation-based learning approaches for Natural Language Processing. Human beings are asked to paraphrase commands and questions for task identification for further execution of a machine. The commands and questions are split into training and validation sets. A total of 483 responses were recorded. Secondly, the training set is paraphrased by the T5 model in order to augment it with further data. Seven state-of-the-art transformer-based text classification algorithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet) are benchmarked for both sets after fine-tuning on the training data for two epochs. We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4.01%. The best result was the RoBERTa model trained on T5 augmented data which achieved 98.96% classification accuracy. Finally, we found that an ensemble of the five best-performing transformer models via Logistic Regression of output label predictions led to an accuracy of 99.59% on the dataset of human responses. A highly-performing model allows the intelligent system to interpret human commands at the social-interaction level through a chatbot-like interface (e.g. "Robot, can we have a conversation?") and allows for better accessibility to AI by non-technical users.