Multi-Task Learning for Document Ranking and Query Suggestion

Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in ICLR, 2018.

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Abstract

We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommen tracks users’ query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users’ underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework.

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@inproceedings{ahmad2018multitask,
  author = {Ahmad, Wasi and Chang, Kai-Wei and Wang, Hongning},
  title = {Multi-Task Learning for Document Ranking and Query Suggestion},
  booktitle = {ICLR},
  year = {2018}
}

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Full Text Slides Code Abstract BibTeX Details

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Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in ICLR, 2018.
Full Text Code Abstract BibTeX Details

We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommen tracks users’ query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users’ underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework.

@inproceedings{ahmad2018multitask,
  author = {Ahmad, Wasi and Chang, Kai-Wei and Wang, Hongning},
  title = {Multi-Task Learning for Document Ranking and Query Suggestion},
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  year = {2018}
}

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Full Text Code Abstract BibTeX Details

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@inproceedings{ahmad2018intent,
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Full Text Code Abstract BibTeX Details

The Gene Ontology (GO) database contains GO terms that describe biological functions of genes.
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@inproceedings{DAECL18,
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  title = {Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions},
  booktitle = {Journal of Computational Biology},
  year = {2018}
}

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