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.

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.

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.

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.