# End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents

arXiv: https://arxiv.org/abs/2107.05541 · HTML: https://mlsanity.com/paper/2107.05541/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Rasa: Open Source Language Understanding and Dialogue Management](https://mlsanity.com/paper/1712.05181/) (2017)
- [DIET: Lightweight Language Understanding for Dialogue Systems](https://mlsanity.com/paper/2004.09936/) (2020)
- [Probabilistic FastText for Multi-Sense Word Embeddings](https://mlsanity.com/paper/1806.02901/) (2018)
- [Hope Speech Detection: A Computational Analysis of the Voice of Peace](https://mlsanity.com/paper/1909.12940/) (2019)
- [Dialogue Transformers](https://mlsanity.com/paper/1910.00486/) (2019)

## Built on by (most cited first, 2 of 2)

- [Modeling Romanized Hindi and Bengali: Dataset Creation and Multilingual LLM Integration](https://mlsanity.com/paper/2511.22769/) (2025)
- [A Benchmark for Bangla Task-Oriented Dialogue Understanding](https://mlsanity.com/paper/2610.18060/) (2026)

## Abstract (arXiv)

Chatbots are intelligent software built to be used as a replacement for human interaction. Existing studies typically do not provide enough support for low-resource languages like Bangla. Due to the increasing popularity of social media, we can also see the rise of interactions in Bangla transliteration (mostly in English) among the native Bangla speakers. In this paper, we propose a novel approach to build a Bangla chatbot aimed to be used as a business assistant which can communicate in low-resource languages like Bangla and Bangla Transliteration in English with high confidence consistently. Since annotated data was not available for this purpose, we had to work on the whole machine learning life cycle (data preparation, machine learning modeling, and model deployment) using Rasa Open Source Framework, fastText embeddings, Polyglot embeddings, Flask, and other systems as building blocks. While working with the skewed annotated dataset, we try out different components and pipelines to evaluate which works best and provide possible reasoning behind the observed results. Finally, we present a pipeline for intent classification and entity extraction which achieves reasonable performance (accuracy: 83.02%, precision: 80.82%, recall: 83.02%, F1-score: 80%).