November 4, 2019
VizSeq: A Visual Analysis Toolkit for Text Generation Tasks
Conference on Empirical Methods in Natural Language Processing (EMNLP)
Automatic evaluation of text generation tasks (e.g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU (Papineni et al., 2002) and ROUGE (Lin, 2004). They, however, are abstract numbers and are not perfectly aligned with human assessment. This suggests inspecting detailed examples as a complement to identify system error patterns. In this paper, we present VizSeq, a visual analysis toolkit for instance-level and corpus-level system evaluation on a wide variety of text generation tasks.
By: Changhan Wang, Anirudh Jain, Danlu Chen, Jiatao Gu
Facebook AI Research
Natural Language Processing & Speech