Overview of the TREC 2022 NeuCLIR Track

Authors: Dawn Lawrie, Sean MacAvaney, James Mayfield, Paul McNamee, Douglas Oard, Luca Soldaini, Eugene Yang

Appeared in: Proceedings of the 31st Text REtrieval Conference (TREC 2022)

Links/IDs:

DBLP journals/corr/abs-2304-12367 arXiv 2304.12367 Google Scholar 7wWfoDgAAAAJ:ns9cj8rnVeAC Semantic Scholar f95b0f61ad894ff917e4d5ba1d957be2d2128d84 smac.pub trec2022-neuclir

Abstract:

This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval. The main task in this year's track was ad hoc ranked retrieval of Chinese, Persian, or Russian newswire documents using queries expressed in English. Topics were developed using standard TREC processes, except that topics developed by an annotator for one language were assessed by a different annotator when evaluating that topic on a different language. There were 172 total runs submitted by twelve teams.

BibTeX @inproceedings{lawrie:trec2022-neuclir, author = {Lawrie, Dawn and MacAvaney, Sean and Mayfield, James and McNamee, Paul and Oard, Douglas and Soldaini, Luca and Yang, Eugene}, title = {Overview of the TREC 2022 NeuCLIR Track}, booktitle = {Proceedings of the 31st Text REtrieval Conference}, year = {2022}, url = {https://arxiv.org/abs/2304.12367} }