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

We describe a new deep learning architecture for learning to rank questionanswer pairs. Our approach extends the long short-term memory (LSTM) networkwith holographic composition to model the relationship between question andanswer representations. As opposed to the neural tensor layer that has beenadopted recently, the holographic composition provides the benefits of scalableand rich representational learning approach without incurring huge parametercosts. Overall, we present Holographic Dual LSTM (HD-LSTM), a unifiedarchitecture for both deep sentence modeling and semantic matching.Essentially, our model is trained end-to-end whereby the parameters of the LSTMare optimized in a way that best explains the correlation between question andanswer representations. In addition, our proposed deep learning architecturerequires no extensive feature engineering. Via extensive experiments, we showthat HD-LSTM outperforms many other neural architectures on two popularbenchmark QA datasets. Empirical studies confirm the effectiveness ofholographic composition over the neural tensor layer.

Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture