Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions

We suggest the use of hash functions to cut down the communication costs when counting subgraphs under edge local differential privacy. While various algorithms exist for computing graph statistics, including the count of subgraphs, under the edge local differential privacy, many suffer with high communication costs, making them less efficient for large graphs. Though data compression is a typical approach in differential privacy, its application in local differential privacy requires a form of compression that every node can reproduce. In our study, we introduce linear congruence hashing. With a sampling rate of $s$, our method can cut communication costs by a factor of $s^2$, albeit at the cost of increasing variance in the published graph statistic by a factor of $s$. The experimental results indicate that, when matched for communication costs, our method achieves a reduction in the $\ell_2$-error for triangle counts by up to 1000 times compared to the performance of leading algorithms.

Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition

Developing high-performing systems for detecting biomedical named entities has major implications. State-of-the-art deep-learning based solutions for entity recognition often require large annotated datasets, which is not available in the biomedical domain. Transfer learning and multi-task learning have been shown to improve performance for low-resource domains. However, the applications of these methods are relatively scarce in the biomedical domain, and a theoretical understanding of why these methods improve the performance is lacking. In this study, we performed an extensive analysis to understand the transferability between different biomedical entity datasets. We found useful measures to predict transferability between these datasets. Besides, we propose combining transfer learning and multi-task learning to improve the performance of biomedical named entity recognition systems, which is not applied before to the best of our knowledge.

Hierarchical Multi Task Learning with Subword Contextual Embeddings for Languages with Rich Morphology

Morphological information is important for many sequence labeling tasks in Natural Language Processing (NLP). Yet, existing approaches rely heavily on manual annotations or external software to capture this information. In this study, we propose using subword contextual embeddings to capture the morphological information for languages with rich morphology. In addition, we incorporate these embeddings in a hierarchical multi-task setting which is not employed before, to the best of our knowledge. Evaluated on Dependency Parsing (DEP) and Named Entity Recognition (NER) tasks, which are shown to benefit greatly from morphological information, our final model outperforms previous state-of-the-art models on both tasks for the Turkish language. Besides, we show a net improvement of 18.86% and 4.61% F-1 over the previously proposed multi-task learner in the same setting for the DEP and the NER tasks, respectively. Empirical results for five different MTL settings show that incorporating subword contextual embeddings brings significant improvements for both tasks. In addition, we observed that multi-task learning consistently improves the performance of the DEP component.