Improved Answer Selection with Pre-Trained Word Embeddings
This work improves answer selection for information retrieval systems, but it is incremental as it combines existing techniques.
The paper tackled answer selection by integrating pre-trained word embeddings into traditional IR systems, achieving significant gains over term frequency methods on three datasets and matching state-of-the-art neural network performance.
This paper evaluates existing and newly proposed answer selection methods based on pre-trained word embeddings. Word embeddings are highly effective in various natural language processing tasks and their integration into traditional information retrieval (IR) systems allows for the capture of semantic relatedness between questions and answers. Empirical results on three publicly available data sets show significant gains over traditional term frequency based approaches in both supervised and unsupervised settings. We show that combining these word embedding features with traditional learning-to-rank techniques can achieve similar performance to state-of-the-art neural networks trained for the answer selection task.