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

Extensive research has recently shown that recurrent neural language modelsare able to process a wide range of grammatical phenomena. How these models areable to perform these remarkable feats so well, however, is still an openquestion. To gain more insight into what information LSTMs base their decisionson, we propose a generalisation of Contextual Decomposition (GCD). Inparticular, this setup enables us to accurately distil which part of aprediction stems from semantic heuristics, which part truly emanates fromsyntactic cues and which part arise from the model biases themselves instead.We investigate this technique on tasks pertaining to syntactic agreement andco-reference resolution and discover that the model strongly relies on adefault reasoning effect to perform these tasks.

Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment