# Multi-Task Learning of Query Intent and Named Entities using Transfer Learning

arXiv: https://arxiv.org/abs/2105.03316 · HTML: https://mlsanity.com/paper/2105.03316/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 28 Apr 2021.

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

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks](https://mlsanity.com/paper/1709.10191/) (2017)

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

- [FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Framework](https://mlsanity.com/paper/2502.02391/) (2025)

## Abstract (arXiv)

Named entity recognition (NER) has been studied extensively and the earlier algorithms were based on sequence labeling like Hidden Markov Models (HMM) and conditional random fields (CRF). These were followed by neural network based deep learning models. Recently, BERT has shown new state of the art accuracy in sequence labeling tasks like NER. In this short article, we study various approaches to task specific NER. Task specific NER has two components - identifying the intent of a piece of text (like search queries), and then labeling the query with task specific named entities. For example, we consider the task of labeling Target store locations in a search query (which could be entered in a search box or spoken in a device like Alexa or Google Home). Store locations are highly ambiguous and sometimes it is difficult to differentiate between say a location and a non-location. For example, "pickup my order at orange store" has "orange" as the store location, while "buy orange at target" has "orange" as a fruit. We explore this difficulty by doing multi-task learning which we call global to local transfer of information. We jointly learn the query intent (i.e. store lookup) and the named entities by using multiple loss functions in our BERT based model and find interesting results.