With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a timeconsuming process. A technique that could add a reference citation at the appropriate place in a sentence will be beneficial. In this perspective, context-aware citation recommendation has been researched upon for around two decades. Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research. However, the lack of wellorganized benchmarking datasets and no model that can attain high performance has made the research difficult. In this paper, we propose a deep learning based model and well-organized dataset for contextaware paper citation recommendation. Our model comprises a document encoder and a context encoder, which uses Graph Convolutional Networks (GCN) layer and Bidirectional Encoder Representations from Transformers (BERT), which is a pretrained model of textual data. By modifying the related PeerRead dataset, we propose a new dataset called FullTextPeerRead containing context sentences to cited references and paper metadata. To the best of our knowledge, This dataset is the first well-organized dataset for context-aware paper recommendation. The results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance and achieve a more than 28% improvement in mean average precision (MAP) and recall@k.

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While the traditional method of deriving representations for documents was bag-of-words, they suffered from high dimensionality and sparsity. Recently, many methods to obtain lower dimensional and densely distributed representations were proposed. Paragraph Vector is one of such algorithms, which extends the word2vec algorithm by considering the paragraph as an additional word. However, it generates a single representation for all tasks, while different tasks may require different representations. In this paper, we propose a Supervised Paragraph Vector, a task-specific variant of Paragraph Vector for situations where class labels exist. Essentially, Supervised Paragraph Vector uses class labels along with words and documents and obtains corresponding representations with respect to the particular classification task. In order to prove the benefits of the proposed algorithm, three performance criteria are used: interpretability, discriminative power, and computational efficiency. To test interpretability, we find words that are close and far to class vectors and demonstrate that such words are closely related to the corresponding class. We also use principal component analysis to visualize all words, documents, and class labels at the same time and show that our method effectively displays the related words and documents for each class label. To evaluate discriminative power and computational efficiency, we perform document classification on four commonly used datasets with various classifiers and achieve comparable classification accuracies to bag-of-words and Paragraph Vector. INDEX TERMS Class label, distributed representations, representation learning, document embedding, word embedding.

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We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English. It is trained with large amounts of generic and biomedical data, using domain tags. Our benchmarks show that it performs near stateof-the-art both on news (generic domain) and biomedical test sets, and that it outperforms the existing publicly released models. We believe that this release will help the large-scale multilingual analysis of the digital content of the COVID-19 crisis and of its effects on society, economy, and healthcare policies. We also release a test set of biomedical text for Korean-English. It consists of 758 sentences from official guidelines and recent papers, all about COVID-19.