Response Selection: A Review of Current Models
Response selection is an important task in natural language processing (NLP) that involves selecting the best response to a given query. It is a core task in dialogue systems, search engines, and other applications that require an automated response to a query. In this review article, we discuss recent advances in response selection models and their applications. We focus on supervised models, which use a training dataset to learn a mapping from query-response pairs to a score indicating the quality of the response.
Supervised models can be divided into two categories: statistical models and neural models. Statistical models are based on statistical methods such as logistic regression and support vector machines (SVMs). They use hand-crafted features to represent the query and response, which are then used to train a model. Neural models, on the other hand, use deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to learn representations of the query and response.
Recently, hybrid models combining both statistical and neural approaches have achieved state-of-the-art results in response selection tasks. These models typically use a statistical model as a first step to identify a set of candidate responses, followed by a neural model to score the candidates. This two-step approach has been shown to be effective for response selection.
In addition to supervised models, there has been research on unsupervised models, which do not require labeled training data. Unsupervised models typically use unsupervised learning techniques such as clustering or matrix factorization to learn representations of the queries and responses. These representations are then used to identify similar queries and responses, which can be used to select a response.
Overall, response selection is a challenging task that has seen significant advances in recent years. Supervised models have shown to be the most effective, and are the focus of ongoing research. Hybrid models combining statistical and neural approaches are particularly promising, and are likely to be the focus of future research. Unsupervised models, while not as effective as supervised models, provide an alternative approach for response selection in situations where labeled training data is not available.
References
Cheng, J., Dong, L., & Lapata, M. (2017). A Survey of Neural Network-Based Natural Language Processing Techniques. Foundations and Trends in Information Retrieval, 11(2-3), 143-264.
Deng, Y., Chen, S., & Yu, Y. (2020). Hybrid Neural-Statistical Response Selection Models for Task-oriented Dialogue System. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 4200-4209.
Li, J., Chen, X., & Smith, N. A. (2016). A Survey of Unsupervised Neural Network-Based Natural Language Processing Techniques. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 1701-1711.