TU Wien @ TREC Deep Learning '19 - Simple Contextualization for Re-ranking

By Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury. 2019

15 citations recorded by the source corpus.

The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time. However, in most instances we have to choose which property is most important to us. We used the opportunity of the TREC 2019 Deep Learning track to evaluate the effectiveness of a balanced neural re-ranking approach. We submitted results of the TK (Transformer-Kernel) model: a neural re-ranking model for ad-hoc search using an efficient contextualization mechanism. TK employs a very small number of lightweight Transformer layers to contextualize query and document word embeddings. To score individual term interactions, we use a document-length enhanced kernel-pooling, which enables users to gain insight into the model. Our best result for the passage ranking task is: 0.420 MAP, 0.671 nDCG, 0.598 P@10 (TUW19-p3 full). Our best result for the document ranking task is: 0.271 MAP, 0.465 nDCG, 0.730 P@10 (TUW19-d3 re-ranking).

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (2020, neutral): “[12] published their Transformer-Kernel (TK) model.”
TU Wien at TREC DL and Podcast 2021: Simple Compression for Dense Retrieval (2021, methodology): “We continued our focus from our previous TREC participations [6, 8] on efficient approaches for retrieval and re-ranking.”
Long Document Ranking with Query-Directed Sparse Transformer (2020, methodology): “• TUW19-d3-re (Hofstätter et al., 2019) as the best method without using non-pretrained language models (non-PLM) in official runs utilizes a transformer to encode both of the query and the document, thereby measuring interactions between terms and scoring the relevance.”