2022
DOI: 10.48550/arxiv.2203.13088
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Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction

Abstract: Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches. This paper proposes ColBERTer, a neural retrieval model using contextualized late interaction (ColBERT) with enhanced reduction. Along the effectiveness Pareto frontier, ColBERTer's reductions dramatically lower ColBERT's storage requirements while simultaneously improving the interpretability of its toke… Show more

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Cited by 2 publications
(5 citation statements)
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“…As 𝜃 increases, terms with lower weights are filtered out; see Eq. (18). Thus, the relevance score between the query and the passage decreases.…”
Section: Performance Of Two-stage Retrievalmentioning
confidence: 98%
See 3 more Smart Citations
“…As 𝜃 increases, terms with lower weights are filtered out; see Eq. (18). Thus, the relevance score between the query and the passage decreases.…”
Section: Performance Of Two-stage Retrievalmentioning
confidence: 98%
“…For a fair comparison, Table 6 only includes models that use the same baseline training strategy as ours. Thus, we exclude approaches that depend on other models for expansion [25,33,51], costly training techniques such as knowledge distillation [9,17,18,38,41,44], or special pretraining [11,20,34] (see Table 8 for more comparisons).…”
Section: Evaluation Of Single Model Fusionmentioning
confidence: 99%
See 2 more Smart Citations
“…These campaigns follow the Cranfield paradigm [9] to create relevance judgements on the pooled output of the participating systems. Recently there has been a growing interest in evaluating the retrieval performance of retrieval models for domain-specific retrieval tasks [2,13,14,27,36] including the medical domain [22,23,35]. Domain-specific retrieval tasks often lack a reliable test collection with human relevance judgments following the Cranfield paradigm [22,27].…”
Section: Introductionmentioning
confidence: 99%