Abstract
Conversational passage retrieval relies on question rewriting to modify the original question so that it no longer depends on the conversation history. Several methods for question rewriting have recently been proposed, but they were compared under different retrieval pipelines. We bridge this gap by thoroughly evaluating those question rewriting methods on the TREC CAsT 2019 and 2020 datasets under the same retrieval pipeline. We analyze the effect of different types of question rewriting methods on retrieval performance and show that by combining question rewriting methods of different types we can achieve state-of-the-art performance on both datasets (Resources can be found at https://github.com/svakulenk0/cast_evaluation.)
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Notes
- 1.
We use ROUGE-1 to measure unigram overlap after punctuation removal, lower casing and Porter stemming. We use the following ROUGE implementation: https://github.com/google-research/google-research/tree/master/rouge.
- 2.
We use the answer passage to the previous turn question retrieved by the automatic rewriting system provided by the TREC CAsT 2020 organizers.
- 3.
Note that our pipeline outperforms the official baseline provided by the TREC CAsT organizers for both 2019 and 2020 datasets for all query rewriting methods they considered. Since our focus is on comparing different query rewriting methods, we do not report those results for brevity.
- 4.
- 5.
Recall that questions in CAsT 2020 may depend on the answer of the previous turn question, but this is not the case in CAsT 2019.
References
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Acknowledgements
We thank Raviteja Anantha for providing the rewrites of the Transformer++ model.
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Vakulenko, S., Voskarides, N., Tu, Z., Longpre, S. (2021). A Comparison of Question Rewriting Methods for Conversational Passage Retrieval. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_43
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DOI: https://doi.org/10.1007/978-3-030-72240-1_43
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