Extreme Classification for Answer Type Prediction in Question Answering
Not on the map. Why?
Abstract (arXiv)
Semantic answer type prediction (SMART) is known to be a useful step towards effective question answering (QA) systems. The SMART task involves predicting the top-k knowledge graph (KG) types for a given natural language question. This is challenging due to the large number of types in KGs. In this paper, we propose use of extreme multi-label classification using Transformer models (XBERT) by clustering KG types using structural and semantic features based on question text. We specifically improve the clustering stage of the XBERT pipeline using textual and structural features derived from KGs. We show that these features can improve end-to-end performance for the SMART task, and yield state-of-the-art results.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 34 references that are in the map. First posted on arXiv 24 Apr 2023.
Builds on
7 of 34 references.
References · 7
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2020The Emerging Trends of Multi-Label Learningcited by 100
- 2020PECOS: Prediction for Enormous and Correlated Output Spacescited by 50
- 2019Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Basecited by 33
- 2017Target Type Identification for Entity-Bearing Queriescited by 2
- 2021Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020cited by 2
Built on by
Not cited yet.