# Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020

arXiv: https://arxiv.org/abs/2109.06714 · HTML: https://mlsanity.com/paper/2109.06714/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## 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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base](https://mlsanity.com/paper/1910.05069/) (2019)
- [Design Patterns for Fusion-Based Object Retrieval](https://mlsanity.com/paper/1708.08715/) (2017)
- [Target Type Identification for Entity-Bearing Queries](https://mlsanity.com/paper/1705.06056/) (2017)
- [SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge](https://mlsanity.com/paper/2012.00555/) (2020)

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

- [Semantic Answer Type and Relation Prediction Task (SMART 2021)](https://mlsanity.com/paper/2112.07606/) (2021)
- [Extreme Classification for Answer Type Prediction in Question Answering](https://mlsanity.com/paper/2304.12395/) (2023)

## Abstract (arXiv)

This paper summarizes our participation in the SMART Task of the ISWC 2020 Challenge. A particular question we are interested in answering is how well neural methods, and specifically transformer models, such as BERT, perform on the answer type prediction task compared to traditional approaches. Our main finding is that coarse-grained answer types can be identified effectively with standard text classification methods, with over 95% accuracy, and BERT can bring only marginal improvements. For fine-grained type detection, on the other hand, BERT clearly outperforms previous retrieval-based approaches.