# TagRec: Automated Tagging of Questions with Hierarchical Learning Taxonomy

arXiv: https://arxiv.org/abs/2107.10649 · HTML: https://mlsanity.com/paper/2107.10649/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 3 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [Multi-class Hierarchical Question Classification for Multiple Choice Science Exams](https://mlsanity.com/paper/1908.05441/) (2019)

## Built on by (most cited first, 6 of 6)

- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [K-12BERT: BERT for K-12 education](https://mlsanity.com/paper/2205.12335/) (2022)
- [TagRec++: Hierarchical Label Aware Attention Network for Question Categorization](https://mlsanity.com/paper/2208.05152/) (2022)
- [Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms](https://mlsanity.com/paper/2301.05150/) (2022)
- [Cross Encoding as Augmentation: Towards Effective Educational Text Classification](https://mlsanity.com/paper/2305.18977/) (2023)
- [Modeling Tag Prediction based on Question Tagging Behavior Analysis of CommunityQA Platform Users](https://mlsanity.com/paper/2307.01420/) (2023)

## Abstract (arXiv)

Online educational platforms organize academic questions based on a hierarchical learning taxonomy (subject-chapter-topic). Automatically tagging new questions with existing taxonomy will help organize these questions into different classes of hierarchical taxonomy so that they can be searched based on the facets like chapter. This task can be formulated as a flat multi-class classification problem. Usually, flat classification based methods ignore the semantic relatedness between the terms in the hierarchical taxonomy and the questions. Some traditional methods also suffer from the class imbalance issues as they consider only the leaf nodes ignoring the hierarchy. Hence, we formulate the problem as a similarity-based retrieval task where we optimize the semantic relatedness between the taxonomy and the questions. We demonstrate that our method helps to handle the unseen labels and hence can be used for taxonomy tagging in the wild. In this method, we augment the question with its corresponding answer to capture more semantic information and then align the question-answer pair's contextualized embedding with the corresponding label (taxonomy) vector representations. The representations are aligned by fine-tuning a transformer based model with a loss function that is a combination of the cosine similarity and hinge rank loss. The loss function maximizes the similarity between the question-answer pair and the correct label representations and minimizes the similarity to unrelated labels. Finally, we perform experiments on two real-world datasets. We show that the proposed learning method outperforms representations learned using the multi-class classification method and other state of the art methods by 6% as measured by Recall@k. We also demonstrate the performance of the proposed method on unseen but related learning content like the learning objectives without re-training the network.