Natural Language Queries
78 papers with code • 1 benchmarks • 2 datasets
To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus.
For evaluation, we adopt TaCoS dataset, and build a new dataset for this task on top of Charades by adding sentence temporal annotations, called Charades-STA.
The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge.
It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries.
Each video in the dataset is annotated with: (1) a human-written free-form NL query, (2) relevant moments in the video w. r. t.
V3CTRON | Data Retrieval & Access System For Flexible Semantic Search & Retrieval Of Proprietary Document Collections Using Natural Language Queries.
V3CTRON is an open source vector database that allows users to upload text based documents & document collections, which are automatically embedded for super-accurate semantic search & retrieval using natural language queries.
This paper explores the task of translating natural language queries into regular expressions which embody their meaning.
The main experimental result in this paper is that a single Neural Programmer model achieves 34. 2% accuracy using only 10, 000 examples with weak supervision.
For a given text query and background API, the tool finds candidate functions by performing a translation from the text to known representations in the API using the semantic parsing approach of Richardson and Kuhn (2017).