SIGMOD 2022 Highlights
The ACM SIGMOD conference is the leading forum for the principles, techniques and applications of database management systems and data management technology.
Extending the Jupyter UX with Custom Widgets: Lessons Learned
As we set out to build a set of powerful in-house interactive annotation tools for NLP/ML tasks, we wanted to share our lessons learned with the community on extending Jupyter notebooks with custom widgets.
ACL 2022 Highlights
This year, Dublin, Ireland hosted ACL 2022, a hybrid conference on computational linguistics (CL) and natural language processing (NLP). Our team sponsored and attended the conference. In this blog, we provide an overview of the invited talks and panel discussions. In addition, we discuss our top paper picks on information extraction, language understanding, prompting, language generation, and explainability, which are also relevant to ongoing research at Megagon Labs.
Low-resource Entity Set Expansion on User-generated Text: Insights and Takeaways
In this work, we investigate the generalizability of existing entity set expansion (ESE) methods to user-generated text as it is widely used in many real-world applications and is known to have more distinctive characteristics than well-written text.
CHI 2022 Conference Highlights
In this blog post, I sum up my experience attending CHI, provide an overview of the keynotes and awards, and summarize several interesting papers on human-AI interaction, mixed-initiative system design, and visualization. Human-centered AI is a key research area of Megagon Labs where we explore challenges related to scalability, usability, and explainability in diverse projects such as data integration, natural language generation, and knowledge graphs. Therefore any research work at the intersection of NLP, data management, and HCI are of significant interest to us.
Generalized Entity Matching with Machop
We use Generalized Entity Matching (GEM) to satisfy these practical requirements and present an end-to-end pipeline, Machop, as the solution. Machop allows end users to define new matching tasks from scratch and apply them to new domains in a step-by-step manner. Machop casts the GEM problem as sequence pair classification so as to utilize the language understanding capability of Transformers-based language models (LMs) such as BERT.
CoCoSum: Summarizing Contrastive and Common Opinions from Reviews
In this blog post, we take one step beyond the current scope of opinion summarization and propose CoCoSum, a framework which aims to generate contrastive and common summaries by comparing multiple entities. This framework consists of two base summarization models that jointly generate contrastive and common summaries.
Megagon Labs Spring 2022 Internship Experience
One of our favorite things to do at Megagon Labs is welcome interns from all over the world into our office. This spring, we had the pleasure of working with two talented interns, Seiji Maekawa and MeiXing Dong. They worked on interesting research such as active learning and information extraction. Below they share their experience […]
Supporting Humans in the Information Extraction Loop: An In-depth Study of the Practices, Limitations, and Opportunities
Through our interviews and follow-up analysis, we provide a fine-grained characterization of information extraction workflows using a task-based model, identify related challenges faced by the human-in-the-loop, and propose design guidelines for addressing these challenges. We now discuss a few of the implications of the task model and our proposed design considerations for developing IE tools.
Megagon Team Profile: Eser Kandogan
As Head of Engineering, Eser helps further develop the skills of the engineers on the team while also helping drive the direction of the lab. He came to us with a wide range of computer science and mentoring experience. Read on to learn more about Eser and get further insight on becoming a successful engineer […]
