Conditional variational autoencoder for query expansion in ad-hoc information retrieval
Query expansion (QE) is commonly used to improve the performance of traditional information retrieval (IR) models. With the adoption of deep learning in IR research, neural QE models have emerged in recent years. Many of these models focus on learning embeddings by leveraging query-document relevanc...
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| Vydané v: | Information sciences Ročník 652; s. 119764 |
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| Jazyk: | English |
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Elsevier Inc
01.01.2024
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Query expansion (QE) is commonly used to improve the performance of traditional information retrieval (IR) models. With the adoption of deep learning in IR research, neural QE models have emerged in recent years. Many of these models focus on learning embeddings by leveraging query-document relevance. These embedding models allow computing semantic similarities between queries and documents to generate expansion terms. However, existing models often ignore query-document interactions. This research aims to address that gap by proposing a QE model using a conditional variational autoencoder. It first maps a query-document pair into a latent space based on their interaction, then estimates an expansion model from that latent space. The proposed model is trained on relevance feedback data and generates expansions using pseudo-relevance feedback at test time. The proposed model is evaluated on three standard TREC collections for document ranking: AP and Robust 04 and GOV02, and the MS MARCO dataset for passage ranking. Results show the model outperforms state-of-the-art traditional and neural QE models. It also demonstrates higher additivity with neural matching than baselines. |
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| AbstractList | Query expansion (QE) is commonly used to improve the performance of traditional information retrieval (IR) models. With the adoption of deep learning in IR research, neural QE models have emerged in recent years. Many of these models focus on learning embeddings by leveraging query-document relevance. These embedding models allow computing semantic similarities between queries and documents to generate expansion terms. However, existing models often ignore query-document interactions. This research aims to address that gap by proposing a QE model using a conditional variational autoencoder. It first maps a query-document pair into a latent space based on their interaction, then estimates an expansion model from that latent space. The proposed model is trained on relevance feedback data and generates expansions using pseudo-relevance feedback at test time. The proposed model is evaluated on three standard TREC collections for document ranking: AP and Robust 04 and GOV02, and the MS MARCO dataset for passage ranking. Results show the model outperforms state-of-the-art traditional and neural QE models. It also demonstrates higher additivity with neural matching than baselines. |
| ArticleNumber | 119764 |
| Author | Huynh, Van-Nam Ou, Wei |
| Author_xml | – sequence: 1 givenname: Wei surname: Ou fullname: Ou, Wei email: studyouwei@gmail.com organization: School of Tourism and Urban-rural Planning, Zhejiang Gongshang University, China – sequence: 2 givenname: Van-Nam surname: Huynh fullname: Huynh, Van-Nam organization: the School of Knowledge Science, Japan Advanced Institute of Science and Technology, Japan |
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| Cites_doi | 10.1561/2200000056 10.1145/2071389.2071390 10.1016/j.ipm.2019.102182 10.1145/366836.366860 10.1145/3196826 10.1109/TASLP.2016.2520371 10.1016/j.ipm.2020.102342 10.1613/jair.1.11259 10.1145/3486250 10.1016/j.ipm.2019.05.009 |
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| Keywords | Conditional variational autoencoder Information retrieval Query expansion Relevance language model |
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| Snippet | Query expansion (QE) is commonly used to improve the performance of traditional information retrieval (IR) models. With the adoption of deep learning in IR... |
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| SubjectTerms | Conditional variational autoencoder Information retrieval Query expansion Relevance language model |
| Title | Conditional variational autoencoder for query expansion in ad-hoc information retrieval |
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