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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Ad hominem attacks are those that target some feature of a person's characterinstead of the position the person is maintaining. These attacks are harmfulbecause they propagate implicit biases and diminish a person's credibility.Since dialogue systems respond directly to user input, it is important to studyad hominems in dialogue responses. To this end, we propose categories of adhominems, compose an annotated dataset, and build a classifier to analyze humanand dialogue system responses to English Twitter posts. We specifically compareresponses to Twitter topics about marginalized communities (#BlackLivesMatter,#MeToo) versus other topics (#Vegan, #WFH), because the abusive language of adhominems could further amplify the skew of power away from marginalizedpopulations. Furthermore, we propose a constrained decoding technique that usessalient $n$-gram similarity as a soft constraint for top-$k$ sampling to reducethe amount of ad hominems generated. Our results indicate that 1) responsesfrom both humans and DialoGPT contain more ad hominems for discussions aroundmarginalized communities, 2) different quantities of ad hominems in thetraining data can influence the likelihood of generating ad hominems, and 3) wecan use constrained decoding techniques to reduce ad hominems in generateddialogue responses.

"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses