Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

By Aliaksei Severyn, Alessandro Moschitti. 2015

764 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Retrieve-and-Edit Framework for Predicting Structured Outputs (2018, background): “Some question answering systems learn to retrieve based on supervision of the correct item to retrieve [35, 27, 19], but these approaches do not apply to our setting since we do not know which items are easy to edit into our target sequence y and must instead estimate this from the embedding.”
Coarse-to-Fine Question Answering for Long Documents (2016, background): “Recently, neural networks models (Wang and Nyberg, 2015; Severyn and Moschitti, 2015; dos Santos et al., 2016) answer sentence selection as latent and generates answer strings instead of selecting text spans.”
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering (2018, methodology): “We tested three different scoring functions: 1) the dot product of p ih and q h , 2) the bilinear form p W q h , and 3) a multilayer perceptron (MLP) (Severyn and Moschitti, 2015).”
Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “tion [146], semantic analysis [147], [148], information retrieval [149], [150], entity recognition [151], [152], relation extraction [153], [154], [155], [156], and event detection [157], [158].”
Challenges in Building Intelligent Open-domain Dialog Systems (2019, methodology): “DSSM is further augmented by introducing Convolutional layers [27, 78, 81] and recurrent layers with Long Short-Term Memory (LSTM) units [62].”
Attentive Pooling Networks (2016, methodology): “In (Severyn & Moschitti, 2015), the authors propose an NN architecture where the representations created by a convolutional layer are the input to similarity measure learning.”
Learning to Match using Local and Distributed Representations of Text for Web Search (2016, background): “Other convolutional models that match short texts using distributed representations include [15, 35], also showing good performance on short text ranking tasks.”
Semi-supervised Question Retrieval with Gated Convolutions (2015, background): “has also involved the use of neural network architectures (Severyn and Moschitti, 2015; Wang and Nyberg, 2015; Shen et al., 2015; Feng et al., 2015; Tan et al., 2015).”
Improved Representation Learning for Question Answer Matching (2016, neutral): “…(Severyn and Moschitti, 2013) 0.678 0.736 (Yih et al., 2013)-BDT 0.694 0.789 (Yih et al., 2013)-LCLR 0.709 0.770 (Wang and Nyberg, 2015) 0.713 0.791 Architecture-II (Feng et al., 2015) 0.711 0.800 (Severyn and Moschitti, 2015) 0.671 0.728 w/o additional features (Severyn and Moschitti, 2015) 0.”
Text feature extraction based on deep learning: a review (2017, background): “Deep learning technology is applied in common NLP (natural language processing) tasks, such as semantic parsing [43], information retrieval [44, 45], semantic role labeling [46, 47], sentimental analysis [48], question answering [49–52], machine translation [53–56], text classification [57], summari”
Denoising Bodies to Titles: Retrieving Similar Questions with Recurrent Convolutional Models (2015, background): “Recent work on answer selection on community QA forums, similar to our task of question retrieval, has also involved the use of neural network architectures (Severyn and Moschitti, 2015; Wang and Nyberg, 2015; Shen et al., 2015; Feng et al., 2015; Tan et al., 2015).”
A Deep Look into Neural Ranking Models for Information Retrieval (2019, methodology): “Example applications include the Convolutional Neural Network for question answering [19].”
DeepStore: In-Storage Acceleration for Intelligent Queries (2019, background): “The recent popular applications include person re-identification [16], style-based music retrieval [72], and question and answering systems [82].”
SelQA: A New Benchmark for Selection-Based Question Answering (2016, background): “Several corpora have been created for these tasks [1, 2, 3], allowing researchers to build effective question answering systems [4, 5, 6] with the aim of improving reading comprehension through understanding and reasoning of natural language.”
A Survey on Text Classification: From Traditional to Deep Learning (2020, methodology): “For example, evaluation metrics such as 𝑃@𝐾 and𝑀𝑖𝑐𝑟𝑜−𝐹1 are used to evaluate multi-label text classification performance, and MRR is usually used to estimate the performance of QA tasks.”
Unseen Class Discovery in Open-world Classification (2018, background): “It is thus related to metric learning (Xing et al., 2002; Yang & Jin, 2006; Bellet et al., 2013; Martin & Jurafsky, 2000; Manning et al., 2008; Severyn & Moschitti, 2015; Guo et al., 2016).”
Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (2017, background): “Due to its adequate model capability and the support of big data, deep neural network (DNN) has achieved a huge success in computer vision [23], speech recognition [16] and natural language processing [8, 38] during the recent ve years.”