This paper addresses an enrichment of translation-based knowledge graph embeddings. When new knowledge triples become available after a knowledge graph is embedded onto a vector space, the embedding should be enriched with the new triples, but without the triples used in training the embedding. The main challenge is that the enrichment of new triples should be accomplished without forgetting the knowledge of current embedding. This paper achieves the goal by minimizing a risk over the new triples penalized by rapid parameter change between old and new embedding models. The effectiveness of the proposed method is shown by learning a translation-based knowledge graph embedding trained incrementally using a series of knowledge triples. The experimental results from two tasks of knowledge graph embedding prove that the proposed method not only incorporates new knowledge of new triples into the existing embedding successfully, but also preserves the knowledge of the current embedding.

BACKGROUND: Advances in medical technology produce highly complex datasets in neurorehabilitation clinics and research laboratories. Artificial neural networks (ANNs) have been utilized to analyze big and complex datasets in various fields, but the u

Multi-behavior recommender systems, rapidly advancing across various domains, utilize plentiful auxiliary interactions on a variety of user behaviors to enhance recommendations for the target behavior, such as purchases. While previous methods have made strides in leveraging such interactions with advanced machine learning methods, they still face challenges in adequately using multi-faceted relationships among behaviors and handling uncertain auxiliary interactions that could potentially lead to purchases or not. In this paper, we propose MuLe (Multi-Grained Graph Learning), a novel graph-based model designed to address these limitations. We design a multi-grained graph learning strategy to capture diverse aspects of behaviors, ranging from unified to specific, and then to target-related behavior interactions. To handle uncertain interactions, we use graph attention, weighting the importance of those interactions related to the target behavior. Afterward, we use an attention mechanism to effectively aggregate diverse behavior embeddings obtained from the multi-grained graph encoders. Extensive experiments show that MuLe significantly outperforms the state-of-the-art methods, achieving improvements of up to 44.6% in HR@10 and 52.9% in NDCG@10, respectively. Our code and datasets are available at https://github.com/geonwooko/MULE.

This paper proposes a novel method to compute how similar two program source codes are. Since a program source code is represented as a structural form, the proposed method adopts convolution kernel functions as a similarity measure. Actually, a program source code has two kinds of structural information. One is syntactic information and the other is the dependencies of function calls lying on the program. Since the syntactic information of a program is expressed as its parse tree, the syntactic similarity between two programs is computed by a parse tree kernel. The function calls within a program provide a global structure of a program and can be represented as a graph. Therefore, the similarity of function calls is computed with a graph kernel. Then, both structural similarities are reflected simultaneously into comparing program source codes by composing the parse tree and the graph kernels based on a cyclomatic complexity. According to the experimental results on a real data set for program plagiarism detection, the proposed method is proved to be effective in capturing the similarity between programs. The experiments show that the plagiarized pairs of programs are found correctly and thoroughly by the proposed method.

Hyun-Je Song, Seong-Bae Park. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

This paper addresses fact correction for abstractive summarization of which aim is to edit a system-generated summary into a new source-consistent summary. The summaries generated by abstractive summarization models often contain various kinds of factual errors. Thus, fact correction becomes essential to apply abstractive summarization to real-world applications. However, most existing methods for fact correction focus only on entity-level errors, which occasions the error correction methods to miss non-entity errors such as inconsistent tokens or mentions. Therefore, this paper presents a token-level fact correction that resolves inconsistencies of a system-generated summary at the token level. Since a token is the smallest meaning-bearing unit, all kinds of errors can be corrected if the errors are rectified at this level. The proposed fact corrector examines the consistency of a summary at the summary level like existing methods, but corrects the found inconsistencies at the token level. Thus, the proposed corrector consists of three modules of a summary fact checker, a token fact checker, and a fact emender. The summary fact checker inspects if a system-generated summary is factually consistent with a source text, the token fact checker finds out the tokens which cause inconsistency, and the fact emender actually replaces the inconsistency-causing tokens with correct tokens in the source text. Since these modules are closely related and affect one another, they are jointly trained to improve the performance of each module. The effectiveness of the proposed fact corrector is empirically proven from two viewpoints of consistency and summarization performance. For correcting inconsistencies in a summary, it is shown that the summaries by the proposed corrector are more factually consistent than those by its competitors. In addition, the proposed corrector outperforms the current state-of-the-art corrector even in automatic summarization performances.