# LSTM-RASA Based Agri Farm Assistant for Farmers

arXiv: https://arxiv.org/abs/2204.09717 · HTML: https://mlsanity.com/paper/2204.09717/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [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)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [Training Neural Response Selection for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1906.01543/) (2019)
- [DIET: Lightweight Language Understanding for Dialogue Systems](https://mlsanity.com/paper/2004.09936/) (2020)
- [Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling](https://mlsanity.com/paper/1601.01530/) (2016)
- [Dialogue Transformers](https://mlsanity.com/paper/1910.00486/) (2019)
- [A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering](https://mlsanity.com/paper/2012.02640/) (2020)
- [How to Generate a Good Word Embedding?](https://mlsanity.com/paper/1507.05523/) (2015)
- [Few-Shot Generalization Across Dialogue Tasks](https://mlsanity.com/paper/1811.11707/) (2018)
- [Building a Question and Answer System for News Domain](https://mlsanity.com/paper/2105.05744/) (2021)

## Built on by (most cited first, 1 of 1)

- [The Future of Internet of Things and Multimodal Language Models in 6G Networks: Opportunities and Challenges](https://mlsanity.com/paper/2504.13971/) (2025)

## Abstract (arXiv)

The application of Deep Learning and Natural Language based ChatBots are growing rapidly in recent years. They are used in many fields like customer support, reservation system and as personal assistant. The Enterprises are using such ChatBots to serve their customers in a better and efficient manner. Even after such technological advancement, the expert advice does not reach the farmers on timely manner. The farmers are still largely dependent on their peers knowledge in solving the problems they face in their field. These technologies have not been effectively used to give the required information to farmers on timely manner. This project aims to implement a closed domain ChatBot for the field of Agriculture Farmers Assistant. Farmers can have conversation with the Chatbot and get the expert advice in their field. Farmers Assistant is based on RASA Open Source Framework. The Chatbot identifies the intent and entity from user utterances and retrieve the remedy from the database and share it with the user. We tested the Bot with existing data and it showed promising results.