Paper Info
Title
Experiments with Convolutional Neural Network Models for Answer Selection
Abstract
In recent years, neural networks have been applied to many text processing problems. One example is learning a similarity function between pairs of text, which has applications to paraphrase extraction, plagiarism detection, question answering, and ad hoc retrieval. Within the information retrieval community, the convolutional neural network model proposed by Severyn and Moschitti in a SIGIR 2015 paper has gained prominence. This paper focuses on the problem of answer selection for question answering: we attempt to replicate the results of Severyn and Moschitti using their open-source code as well as to reproduce their results via a de novo (i.e., from scratch) implementation using a completely different deep learning toolkit. Our de novo implementation is instructive in ascertaining whether reported results generalize across toolkits, each of which have their idiosyncrasies. We were able to successfully replicate and reproduce the reported results of Severyn and Moschitti, albeit with minor differences in effectiveness, but affirming the overall design of their model. Additional ablation experiments break down the components of the model to show their contributions to overall effectiveness. Interestingly, we find that removing one component actually increases effectiveness and that a simplified model with only four word overlap features performs surprisingly well, even better than convolution feature maps alone.
Year
DOI
Venue
2017
10.1145/3077136.3080648
SIGIR
Field
DocType
ISBN
Data mining,Plagiarism detection,Convolutional neural network,Computer science,Paraphrase,Natural language processing,Artificial intelligence,Deep learning,Artificial neural network,Replicate,Text processing,Question answering,Information retrieval,Machine learning
Conference
978-1-4503-5022-8
Citations 
PageRank 
References 
7
0.51
11
Authors
3
Name
Order
Citations
PageRank
Jinfeng Rao18110.26
Hua He2151.66
Jimmy Lin34800376.93