English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A question answering (QA) system is a type of conversational AI thatgenerates natural language answers to questions posed by human users. QAsystems often form the backbone of interactive dialogue systems, and have beenstudied extensively for a wide variety of tasks ranging from restaurantrecommendations to medical diagnostics. Dramatic progress has been made inrecent years, especially from the use of encoder-decoder neural architecturestrained with big data input. In this paper, we take initial steps to bringingstate-of-the-art neural QA technologies to Software Engineering applications bydesigning a context-based QA system for basic questions about subroutines. Wecurate a training dataset of 10.9 million question/context/answer tuples basedon rules we extract from recent empirical studies. Then, we train a customneural QA model with this dataset and evaluate the model in a study withprofessional programmers. We demonstrate the strengths and weaknesses of thesystem, and lay the groundwork for its use in eventual dialogue systems forsoftware engineering.

A Neural Question Answering System for Basic Questions about Subroutines