Deep neural scoring models have recently been shown to improve ranking quality on a number of benchmarks (Guo et al., 2016; Daiet al., 2018; MacAvaney et al., 2019; Yanget al., 2019a). However, these methods rely on underlying ad-hoc retrieval systems to generate candidates for scoring, which are rarely neural themselves (Zamani et al., 2018). Re-cent work has shown that the performance of ad-hoc neural retrieval systems can be competitive with a number of baselines (Zamani et al.,2018), potentially leading the way to full end-to-end neural retrieval. A major road-block to the adoption of ad-hoc retrieval models is that they require large supervised training sets to surpass classic term-based techniques, which can be developed from raw corpora. Previous work shows weakly supervised data can yield competitive results, e.g., click data (Dehghaniet al., 2017; Borisov et al., 2016). Unfortunately for many domains, even weakly supervised data can be scarce. In this paper, we pro-pose an approach to zero-shot learning (Xianet al., 2018) for ad-hoc retrieval models that relies on synthetic query generation . Crucially, the query generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, query-document relevance pairs that are domain targeted. On a number of benchmarks, we show that this is an effective strategy for building neural retrieval models for specialised domains .
A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass traditional term-based techniques, which are constructed from raw corpora. In this paper, we propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. The question generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, question-passage relevance pairs that are domain specific. Furthermore, when this is coupled with a simple hybrid term-neural model, first-stage retrieval performance can be improved further. Empirically, we show that this is an effective strategy for building neural passage retrieval models in the absence of large training corpora. Depending on the domain, this technique can even approach the accuracy of supervised models .
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Deep neural scoring models have recently been shown to improve ranking quality on a number of benchmarks (Guo et al., 2016; Daiet al., 2018; MacAvaney et al., 2019; Yanget al., 2019a). However, these methods rely on underlying ad-hoc retrieval systems to generate candidates for scoring, which are rarely neural themselves (Zamani et al., 2018). Re-cent work has shown that the performance of ad-hoc neural retrieval systems can be competitive with a number of baselines (Zamani et al.,2018), potentially leading the way to full end-to-end neural retrieval. A major road-block to the adoption of ad-hoc
A major obstacle to the wide-spread adoption of neural
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classic
traditional
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can be developed
are constructed
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Previous work shows weakly supervised data can yield competitive results, e.g., click data (Dehghaniet al., 2017; Borisov et al., 2016). Unfortunately for many domains, even weakly supervised data can be scarce.
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pro-pose
propose
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(Xianet al., 2018) for ad-hoc retrieval models that relies on synthetic query generation . Crucially, the query
for passage retrieval that uses synthetic question generation to close this gap. The question
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query-document
question-passage
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targeted. On a number of benchmarks,
specific. Furthermore, when this is coupled with a simple hybrid term-neural model, first-stage retrieval performance can be improved further. Empirically,
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retrieval models for specialised domains
passage retrieval models in the absence of large training corpora. Depending on the domain, this technique can even approach the accuracy of supervised models