# Deep Learning for Answer Sentence Selection

arXiv: https://arxiv.org/abs/1412.1632 · HTML: https://mlsanity.com/paper/1412.1632/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2014.

## Built on by (most cited first, 17 of 17)

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- [Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning](https://mlsanity.com/paper/2111.01415/) (2021)
- [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)
- [Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing](https://mlsanity.com/paper/2302.01786/) (2023)
- [A Coarse to Fine Question Answering System based on Reinforcement Learning](https://mlsanity.com/paper/2106.00257/) (2021)
- [Machine Reading Comprehension using Case-based Reasoning](https://mlsanity.com/paper/2305.14815/) (2023)
- [Feature Engineering in Learning-to-Rank for Community Question Answering Task](https://mlsanity.com/paper/2309.07610/) (2023)
- [Efficient semantic uncertainty quantification in language models via diversity-steered sampling](https://mlsanity.com/paper/2510.21310/) (2025)
- [A Multi-Size Neural Network with Attention Mechanism for Answer Selection](https://mlsanity.com/paper/2105.03278/) (2021)
- [Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification](https://mlsanity.com/paper/2109.01560/) (2021)
- [Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information](https://mlsanity.com/paper/2309.11294/) (2023)

## Abstract (arXiv)

Answer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger context of open domain question answering. We propose a novel approach to solving this task via means of distributed representations, and learn to match questions with answers by considering their semantic encoding. This contrasts prior work on this task, which typically relies on classifiers with large numbers of hand-crafted syntactic and semantic features and various external resources. Our approach does not require any feature engineering nor does it involve specialist linguistic data, making this model easily applicable to a wide range of domains and languages. Experimental results on a standard benchmark dataset from TREC demonstrate that---despite its simplicity---our model matches state of the art performance on the answer sentence selection task.