# FAQ-based Question Answering via Word Alignment

arXiv: https://arxiv.org/abs/1507.02628 · HTML: https://mlsanity.com/paper/1507.02628/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2015.

## Built on by (most cited first, 3 of 3)

- [On Length Divergence Bias in Textual Matching Models](https://mlsanity.com/paper/2109.02431/) (2021)
- [SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation](https://mlsanity.com/paper/2206.10029/) (2022)
- [Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models](https://mlsanity.com/paper/2507.03726/) (2025)

## Abstract (arXiv)

In this paper, we propose a novel word-alignment-based method to solve the FAQ-based question answering task. First, we employ a neural network model to calculate question similarity, where the word alignment between two questions is used for extracting features. Second, we design a bootstrap-based feature extraction method to extract a small set of effective lexical features. Third, we propose a learning-to-rank algorithm to train parameters more suitable for the ranking tasks. Experimental results, conducted on three languages (English, Spanish and Japanese), demonstrate that the question similarity model is more effective than baseline systems, the sparse features bring 5% improvements on top-1 accuracy, and the learning-to-rank algorithm works significantly better than the traditional method. We further evaluate our method on the answer sentence selection task. Our method outperforms all the previous systems on the standard TREC data set.