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/

Zendy Plus

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

Zendy Tools

Zendy Open

We present assertion based question answering (ABQA), an open domain questionanswering task that takes a question and a passage as inputs, and outputs asemi-structured assertion consisting of a subject, a predicate and a list ofarguments. An assertion conveys more evidences than a short answer span inreading comprehension, and it is more concise than a tedious passage inpassage-based QA. These advantages make ABQA more suitable for human-computerinteraction scenarios such as voice-controlled speakers. Further progresstowards improving ABQA requires richer supervised dataset and powerful modelsof text understanding. To remedy this, we introduce a new dataset calledWebAssertions, which includes hand-annotated QA labels for 358,427 assertionsin 55,960 web passages. To address ABQA, we develop both generative andextractive approaches. The backbone of our generative approach is sequence tosequence learning. In order to capture the structure of the output assertion,we introduce a hierarchical decoder that first generates the structure of theassertion and then generates the words of each field. The extractive approachis based on learning to rank. Features at different levels of granularity aredesigned to measure the semantic relevance between a question and an assertion.Experimental results show that our approaches have the ability to inferquestion-aware assertions from a passage. We further evaluate our approaches byincorporating the ABQA results as additional features in passage-based QA.Results on two datasets show that ABQA features significantly improve theaccuracy on passage-based~QA.

Assertion-based QA with Question-Aware Open Information Extraction