To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called “CoaCor”), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.
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Semantic query-by-example speech search using visual grounding
A number of recent studies have started to investigate how
speech systems can be trained on untranscribed speech by
leveraging accompanying images at training time. Examples
of tasks include keyword prediction and within- and acrossmode retrieval. Here we consider how such models can be
used for query-by-example (QbE) search, the task of retrieving
utterances relevant to a given spoken query. We are particularly interested in semantic QbE, where the task is not only to
retrieve utterances containing exact instances of the query, but
also utterances whose meaning is relevant to the query. We follow a segmental QbE approach where variable-duration speech
segments (queries, search utterances) are mapped to fixeddimensional embedding vectors. We show that a QbE system
using an embedding function trained on visually grounded
speech data outperforms a purely acoustic QbE system in
terms of both exact and semantic retrieval performance.
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- Award ID(s):
- 1816627
- NSF-PAR ID:
- 10108790
- Date Published:
- Journal Name:
- ICASSP proceedings
- ISSN:
- 0736-7791
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
