Decoding a Neural Retriever's Latent Space for Query Suggestion

• URL: http://arxiv.org/abs/2210.12084v1
• Date: Fri, 21 Oct 2022 16:19:31 GMT
• Title: Decoding a Neural Retriever's Latent Space for Query Suggestion
• Authors: Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann
• Abstract summary: We show that it is possible to decode a meaningful query from its latent representation and, when moving in the right direction in latent space, to decode a query that retrieves the relevant paragraph. We employ the query decoder to generate a large synthetic dataset of query reformulations for MSMarco. On this data, we train a pseudo-relevance feedback (PRF) T5 model for the application of query suggestion.
• Score: 28.410064376447718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice. However, neural systems lack the interpretability of bag-of-words models; it is not trivial to connect a query change to a change in the latent space that ultimately determines the retrieval results. To shed light on this embedding space, we learn a "query decoder" that, given a latent representation of a neural search engine, generates the corresponding query. We show that it is possible to decode a meaningful query from its latent representation and, when moving in the right direction in latent space, to decode a query that retrieves the relevant paragraph. In particular, the query decoder can be useful to understand "what should have been asked" to retrieve a particular paragraph from the collection. We employ the query decoder to generate a large synthetic dataset of query reformulations for MSMarco, leading to improved retrieval performance. On this data, we train a pseudo-relevance feedback (PRF) T5 model for the application of query suggestion that outperforms both query reformulation and PRF information retrieval baselines.

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