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

Implicit Discourse Relation Recognition (IDRR) is a sophisticated andchallenging task to recognize the discourse relations between the argumentswith the absence of discourse connectives. The sense labels for each discourserelation follow a hierarchical classification scheme in the annotation process(Prasad et al., 2008), forming a hierarchy structure. Most existing works donot well incorporate the hierarchy structure but focus on the syntax featuresand the prior knowledge of connectives in the manner of pure textclassification. We argue that it is more effective to predict the paths insidethe hierarchical tree (e.g., "Comparison -> Contrast -> however") rather thanflat labels (e.g., Contrast) or connectives (e.g., however). We propose aprompt-based path prediction method to utilize the interactive information andintrinsic senses among the hierarchy in IDRR. This is the first work thatinjects such structure information into pre-trained language models via prompttuning, and the performance of our solution shows significant and consistentimprovement against competitive baselines.

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition