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

This paper addressed the problem of structured sentiment analysis using abi-affine semantic dependency parser, large pre-trained language models, andpublicly available translation models. For the monolingual setup, weconsidered: (i) training on a single treebank, and (ii) relaxing the setup bytraining on treebanks coming from different languages that can be adequatelyprocessed by cross-lingual language models. For the zero-shot setup and a giventarget treebank, we relied on: (i) a word-level translation of availabletreebanks in other languages to get noisy, unlikely-grammatical, but annotateddata (we release as much of it as licenses allow), and (ii) merging thosetranslated treebanks to obtain training data. In the post-evaluation phase, wealso trained cross-lingual models that simply merged all the English treebanksand did not use word-level translations, and yet obtained better results.According to the official results, we ranked 8th and 9th in the monolingual andcross-lingual setups.

LyS_ACoru\~na at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing