Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learning query-document relevance patterns, but few explorations have been done on understanding the text content of a query or a document. This paper studies leveraging a recently-proposed contextual neural language model, BERT, to provide deeper text understanding for IR.Experimental results demonstrate that the contextual text representations from BERT are more effective than traditional word embeddings. Compared to bag-of-words retrieval models, the contextual language model can better leverage language structures, bringing large improvements on queries written in natural languages. Combining the text understanding ability with search knowledge leads to an enhanced pre-trained BERT model that can benefit related search tasks where training data are limited.
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Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation
For most queries, the set of relevant documents spans multiple
subtopics. Inspired by the neural ranking models and query-specific
neural clustering models, we develop Topic-Mono-BERT which
performs both tasks jointly. Based on text embeddings of BERT, our
model learns a shared embedding that is optimized for both tasks.
The clustering hypothesis would suggest that embeddings which
place topically similar text in close proximity will also perform
better on ranking tasks. Our model is trained with the Wikimarks
approach to obtain training signals for relevance and subtopics on
the same queries.
Our task is to identify overview passages that can be used to
construct a succinct answer to the query. Our empirical evaluation
on two publicly available passage retrieval datasets suggests that
including the clustering supervision in the ranking model leads to
about 16% improvement in identifying text passages that summarize
different subtopics within a query.
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- Award ID(s):
- 1846017
- NSF-PAR ID:
- 10473540
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400700231
- Format(s):
- Medium: X
- Location:
- Kolkata India
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Proceeding:
- https://doi.org/10.1145/3574318
