# Feature Engineering in Learning-to-Rank for Community Question Answering Task

arXiv: https://arxiv.org/abs/2309.07610 · HTML: https://mlsanity.com/paper/2309.07610/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [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)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [A Deep Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/1711.08611/) (2017)
- [TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank](https://mlsanity.com/paper/1812.00073/) (2018)
- [Deep Learning for Answer Sentence Selection](https://mlsanity.com/paper/1412.1632/) (2014)
- [Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture](https://mlsanity.com/paper/1707.06372/) (2017)
- [aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model](https://mlsanity.com/paper/1801.01641/) (2018)
- [Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer](https://mlsanity.com/paper/2005.08294/) (2020)
- [Attention-based model for predicting question relatedness on Stack Overflow](https://mlsanity.com/paper/2103.10763/) (2021)
- [Learning to Rank Questions for Community Question Answering with Ranking SVM](https://mlsanity.com/paper/1608.04185/) (2016)
- [SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network](https://mlsanity.com/paper/2006.04084/) (2020)

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

- [A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data](https://mlsanity.com/paper/2311.10025/) (2023)
- [An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank](https://mlsanity.com/paper/2310.13269/) (2023)

## Abstract (arXiv)

Community question answering (CQA) forums are Internet-based platforms where users ask questions about a topic and other expert users try to provide solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer, StackExchange exist with a lot of user-generated data. These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the query of the user. In this work, we empirically investigate a few aspects of this domain. Firstly, in addition to traditional features like TF-IDF, BM25 etc., we introduce a BERT-based feature that captures the semantic similarity between the question and answer. Secondly, most of the existing research works have focused on features extracted only from the question part; features extracted from answers have not been explored extensively. We combine both types of features in a linear fashion. Thirdly, using our proposed concepts, we conduct an empirical investigation with different rank-learning algorithms, some of which have not been used so far in CQA domain. On three standard CQA datasets, our proposed framework achieves state-of-the-art performance. We also analyze importance of the features we use in our investigation. This work is expected to guide the practitioners to select a better set of features for the CQA retrieval task.