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.
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.
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.
