# A Compare-Aggregate Model with Latent Clustering for Answer Selection

arXiv: https://arxiv.org/abs/1905.12897 · HTML: https://mlsanity.com/paper/1905.12897/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 19 of 19)

- [Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering](https://mlsanity.com/paper/2109.07009/) (2021)
- [Joint Models for Answer Verification in Question Answering Systems](https://mlsanity.com/paper/2107.04217/) (2021)
- [Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection](https://mlsanity.com/paper/2205.10455/) (2022)
- [Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge](https://mlsanity.com/paper/2104.05216/) (2021)
- [Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation](https://mlsanity.com/paper/2110.07150/) (2021)
- [Question-Answer Sentence Graph for Joint Modeling Answer Selection](https://mlsanity.com/paper/2203.03549/) (2022)
- [Knowledge Transfer from Answer Ranking to Answer Generation](https://mlsanity.com/paper/2210.12865/) (2022)
- [Double Retrieval and Ranking for Accurate Question Answering](https://mlsanity.com/paper/2201.05981/) (2022)
- [Multilingual Answer Sentence Reranking via Automatically Translated Data](https://mlsanity.com/paper/2102.10250/) (2021)
- [Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems](https://mlsanity.com/paper/2201.05767/) (2022)
- [RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm](https://mlsanity.com/paper/2301.02807/) (2023)
- [In Situ Answer Sentence Selection at Web-scale](https://mlsanity.com/paper/2201.05984/) (2022)
- [A Prototype-Oriented Clustering for Domain Shift with Source Privacy](https://mlsanity.com/paper/2302.03807/) (2023)
- [Reference-based Weak Supervision for Answer Sentence Selection using Web Data](https://mlsanity.com/paper/2104.08943/) (2021)
- [Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation](https://mlsanity.com/paper/2106.11096/) (2021)
- [Context-Aware Transformer Pre-Training for Answer Sentence Selection](https://mlsanity.com/paper/2305.15358/) (2023)
- [Hierarchical Ranking for Answer Selection](https://mlsanity.com/paper/2102.00677/) (2021)
- [Classical Sequence Match is a Competitive Few-Shot One-Class Learner](https://mlsanity.com/paper/2209.06394/) (2022)
- [Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection](https://mlsanity.com/paper/2306.02196/) (2023)

## Abstract (arXiv)

In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing. First, we explore the effect of additional information by adopting a pretrained language model to compute the vector representation of the input text and by applying transfer learning from a large-scale corpus. Second, we enhance the compare-aggregate model by proposing a novel latent clustering method to compute additional information within the target corpus and by changing the objective function from listwise to pointwise. To evaluate the performance of the proposed approaches, experiments are performed with the WikiQA and TREC-QA datasets. The empirical results demonstrate the superiority of our proposed approach, which achieve state-of-the-art performance for both datasets.