Entity linking (EL) on short text is crucial for a variety of industrial applications. Compared with general long-text EL, short-text EL poses particular challenges as the limited context restricts the clues one can leverage to disambiguate textual mentions. On the other hand, existing studies mostly focus on black-box neural methods and thus lack interpretability, which is critical to industrial applications in certain areas. In this study, we extend upon LNN-EL, a monolingual short-text EL method based on interpretable first-order logic, by incorporating three sets of multilingual features to enable disambiguating mentions written in languages other than English. More specifically, we use multilingual autoencoding language models (i.e., mBERT) to capture the similarities between the mention with its context and the candidate entity; we use multilingual sequence-to-sequence language models (i.e., mBART and mT5) to represent the likelihood of the text given the candidate entity. We also propose a word-level context feature to capture the semantic evidence of the co-occurring mentions. We evaluate the proposed xLNN-EL approach on the QALD-9-multilingual dataset and demonstrate the cross-linguality of the model and the effectiveness of the features.

In the past few years, large pre-trained language models (PLMs) have been widely adopted in different areas and have made fundamental improvements over a variety of downstream tasks in natural language processing (NLP). Meanwhile, domain-specific variants of PLMs are being proposed to address the needs of domains that demonstrate a specific pattern of writing and vocabulary, e.g., BioBERT for the biomedical domain and ClinicalBERT for the clinical domain. Recently, generative language models like BART and T5 are gaining popularity with their competitive performance on text generation as well as on tasks cast as generative problems. However, in the clinical domain, such domain-specific generative variants are still underexplored. To address this need, our work introduces a T5-based text-to-text transformer model pre-trained on clinical text, i.e., ClinicalT5. We evaluate the proposed model both intrinsically and extrinsically over a diverse set of tasks across multiple datasets, and show that ClinicalT5 dramatically outperforms T5 in the domain-specific tasks and compares favorably with its close baselines.

Domain-specific pre-trained language models (PLMs) have achieved great success over various downstream tasks in different domains. However, existing domain-specific PLMs mostly rely on self-supervised learning over large amounts of domain text, without explicitly integrating domain-specific knowledge, which can be essential in many domains. Moreover, in knowledge-sensitive areas such as the biomedical domain, knowledge is stored in multiple sources and formats, and existing biomedical PLMs either neglect them or utilize them in a limited manner. In this work, we introduce an architecture to integrate domain knowledge from diverse sources into PLMs in a parameter-efficient way. More specifically, we propose to encode domain knowledge via adapters, which are small bottleneck feed-forward networks inserted between intermediate transformer layers in PLMs. These knowledge adapters are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller to enrich PLMs. Taking the biomedical domain as a case study, we explore three knowledge-specific adapters for PLMs based on the UMLS Metathesaurus graph, the Wikipedia articles for diseases, and the semantic grouping information for biomedical concepts. Extensive experiments on different biomedical NLP tasks and datasets demonstrate the benefits of the proposed architecture and the knowledge-specific adapters across multiple PLMs.

Entity linking (EL) is the task of disambiguating mentions appearing in text by linking them to entities in a knowledge graph, a crucial task for text understanding, question answering or conversational systems. In the special case of short-text EL, which poses additional challenges due to limited context, prior approaches have reached good performance by employing heuristics-based methods or purely neural approaches. Here, we take a different, neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to use rules, we show that we reach competitive or better performance with SoTA black-box neural approaches. Furthermore, our framework has the benefits of extensibility and transferability. We show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even with scores resulting from previous EL methods, thus improving on such methods. As an example of improvement, on the LC-QuAD-1.0 dataset, we show more than 3% increase in F1 score relative to previous SoTA. Finally, we show that the inductive bias offered by using logic results in a set of learned rules that transfers from one dataset to another, sometimes without finetuning, while still having high accuracy.

Network representation learning (NRL) is crucial in the area of graph learning. Recently, graph autoencoders and its variants have gained much attention and popularity among various types of node embedding approaches. Most existing graph autoencoder-based methods aim to minimize the reconstruction errors of the input network while not explicitly considering the semantic relatedness between nodes. In this paper, we propose a novel network embedding method which models the consistency across different views of networks. More specifically, we create a second view from the input network which captures the relation between nodes based on node content and enforce the latent representations from the two views to be consistent by incorporating a multiview adversarial regularization module. The experimental studies on benchmark datasets prove the effectiveness of this method, and demonstrate that our method compares favorably with the state-of-the-art algorithms on challenging tasks such as link prediction and node clustering. We also evaluate our method on a real-world application, i.e., 30-day unplanned ICU readmission prediction, and achieve promising results compared with several baseline methods.