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.
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.
it is important to develop a framework to explain the results of DL-based EM models. We developed Minun, a model-agnostic framework to provide local explanations for black-box EM models. Minun employs counterfactual examples of entity pairs as the explanation.
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.