Beibei Gao, Yangsen Zhang, Ga Xiang, Yushan Jiang. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Feature engineering is a classical problem in recommender systems, and feature interactions is one of the most important parts of feature engineering. Factorization based models are widely used for explicit feature interactions. However, most current works utilize separate features to model cross features. Such a pattern limits the significance of cross features, since realistic recommendation scenarios are rich in associations between features. In this paper, we classify the basic feature interactions into sum-interaction and product-interaction, and improve the current general strategy of explicit feature interactions. Based on these theoretical studies, we propose a novel explicit feature interactions model Attentional Aggregative Interaction Network (AAIN), which models higher-order features using a cyclic explicit module. Specifically, we introduce attention mechanism for the reorganization of separate features, followed by product-interaction and higher-order features’ compression and output. The model is efficient since: 1) AAIN automatically learns high-order feature interactions and filters them with different weights. 2) AAIN optimizes the interaction between features into the interaction between feature groups, which allows for other relevant information to be considered when performing interactions. Furthermore, we integrate AAIN model with the classical deep neural network (DNN) model into a new model Deep Attentional Aggregative Interaction Network (DAAIN). Experiments on real-world datasets show that our models achieve state-of-the-art results.

Event corpora are imperative to train event extraction models. Currently, most existing event corpora suffer from being available only in English, and their construction is limited by high annotation costs. This paper aims to construct a corpus that concerns social security causality events in Chinese and proposes a faster and less expensive construction method. The contributions are as follows: (i) An event corpus SSECau for the social security field in Chinese is constructed. They are from 2,235 web texts and microblogs, with event causality annotated at the document level. (ii) A corpus construction method with manual annotation and machine pre-tagging is proposed to improve accuracy and speed. (iii) A pre-tagging method based on BiLSTM-CRF (bidirectional long short-term memory and conditional random field) is deployed to extract events automatically. The experimental results show the best consistency between automatic pre-tagging and manual annotation can reach up to 82 %; while the dynamic tagging process improves both the labeling speed and accuracy. The SSECau corpus can aid the development and evaluation of event extraction models for the social security field; annotated cause-effect relationships at the document level can potentially enhance the training of complex extraction models; the proposed dynamic process with pre-tagging can serve as a reference for future corpus construction.

Assigning paper to suitable reviewers is of great significance to ensure the accuracy and fairness of peer review results. In the past three decades, many researchers have made a wealth of achievements on the reviewer assignment problem (RAP). In this survey, we provide a comprehensive review of the primary research achievements on reviewer assignment algorithm from 1992 to 2022. Specially, this survey first discusses the background and necessity of automatic reviewer assignment, and then systematically summarize the existing research work from three aspects, i.e., construction of candidate reviewer database, computation of matching degree between reviewers and papers, and reviewer assignment optimization algorithm, with objective comments on the advantages and disadvantages of the current algorithms. Afterwards, the evaluation metrics and datasets of reviewer assignment algorithm are summarized. To conclude, we prospect the potential research directions of RAP. Since there are few comprehensive survey papers on reviewer assignment algorithm in the past ten years, this survey can serve as a valuable reference for the related researchers and peer review organizers.

The mobile social network contains a large amount of information in a form of commentary. Effective analysis of the sentiment in the comments would help improve the recommendations in the mobile network. With the development of well‐performing pretrained language models, the performance of sentiment classification task based on deep learning has seen new breakthroughs in the past decade. However, deep learning models suffer from poor interpretability, making it difficult to integrate sentiment knowledge into the model. This paper proposes a sentiment classification model based on the cascade of the BERT model and the adaptive sentiment dictionary. First, the pretrained BERT model is used to fine‐tune with the training corpus, and the probability of sentiment classification in different categories is obtained through the softmax layer. Next, to allow a more effective comparison between the probabilities for the two classes, a nonlinearity is introduced in a form of positive‐negative probability ratio, using the rule method based on sentiment dictionary to deal with the probability ratio below the threshold. This method of cascading the pretrained model and the semantic rules of the sentiment dictionary allows to utilize the advantages of both models. Different sized Chnsenticorp data sets are used to train the proposed model. Experimental results show that the Dict‐BERT model is better than the BERT‐only model, especially when the training set is relatively small. The improvement is obvious with the accuracy increase of 0.8%.