We introduce Sudowoodo, an end-to-end framework for a variety of data integration applications to resolve the limitations of data integration. Sudowoodo addresses the label requirement by leveraging contrastive learning to learn a data representation model from a large collection of unlabeled data items. This is realized by the contrastive objective that allows the model to learn how to distinguish pairs of similar data items from dissimilar ones that are likely to be distinct.

In this workshop, we are interested in (but not restricted to) the dimensions of matching (see Figure 1), as well as their resultant combinations. Recent advances in diverse communities from artificial intelligence and databases to computational linguistics and human-computer interaction, have demonstrated promising results in different matching tasks related to the previously mentioned (and many other) domains.

In this blog post, we present MEGAnno, our flexible, exploratory, efficient, and seamless labeling framework for NLP researchers and practitioners. In short, MEGAnno aims to reduce costs while improving the quality of labeling.

2022 was a very productive year for our research team to bring forth many ideas to fruition as published papers in top conferences and workshops in the fields of natural language processing, machine learning, data Management, and human-computer interaction.

Megagon Labs researchers proposed a new CQA Summarization task focused on summarizing QA pairs in Community-based Question Answering. In addition, we developed a multi-stage annotation framework and created a benchmark CoQASum for the CQA Summarization task.