This paper presents EvAlign, a visual analytics framework for quantitative and qualitative evaluation of automatic translation alignment models. EvAlign offers various visualization views enabling developers to visualize their models’ predictions and compare the performance of their models with other baseline and state-of-the-art models. Through different search and filter functions, researchers and practitioners can also inspect the frequent alignment errors and their positions. EvAlign hosts nine gold standard datasets and the predictions of multiple alignment models. The tool is extendable, and adding additional datasets and models is straightforward. EvAlign can be deployed and used locally and is available on GitHub.

This paper introduces Summary Explorer, a new tool to support the manual inspection of text summarization systems by compiling the outputs of 55 state-of-the-art single document summarization approaches on three benchmark datasets, and visually exploring them during a qualitative assessment. The underlying design of the tool considers three well-known summary quality criteria (coverage, faithfulness, and position bias), encapsulated in a guided assessment based on tailored visualizations. The tool complements existing approaches for locally debugging summarization models and improves upon them. The tool is available at https://tldr.webis.de/