id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020841-40f2p3t4	Hofstätter, Sebastian	Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-ranking Results	2020-03-24		.txt	text/plain	1526	91	53	In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results. We present the content-focused Neural-IR-Explorer, which empowers users to browse through retrieval results and inspect the inner workings and fine-grained results of neural re-ranking models. The explorer complements metrics based evaluation, by focusing on the content of queries and documents, and how the neural models relate them to each other. Users can explore each query result in more detail: We show the internal partial scores and content of the returned documents with different highlighting modes to surface the inner workings of a neural re-ranking model. The explorer displays data created by a batched evaluation run of a neural re-ranking model. Additionally, the Neural-IR-Explorer also illuminates the pool bias [12] of the MSMARCO ranking collection: The small number of judged documents per query makes the evaluation fragile. We presented the content-focused Neural-IR-Explorer to complement metric based evaluation of retrieval models.	./cache/cord-020841-40f2p3t4.txt	./txt/cord-020841-40f2p3t4.txt