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

Personalized chatbots focus on endowing the chatbots with a consistentpersonality to behave like real users and further act as personal assistants.Previous studies have explored generating implicit user profiles from theuser's dialogue history for building personalized chatbots. However, thesestudies only use the response generation loss to train the entire model, thusit is prone to suffer from the problem of data sparsity. Besides, theyoveremphasize the final generated response's quality while ignoring thecorrelations and fusions between the user's dialogue history, leading to roughdata representations and performance degradation. To tackle these problems, wepropose a self-supervised learning framework MCP for capturing betterrepresentations from users' dialogue history for personalized chatbots.Specifically, we apply contrastive sampling methods to leverage the supervisedsignals hidden in user dialog history, and generate the pre-training samplesfor enhancing the model. We design three pre-training tasks based on threetypes of contrastive pairs from user dialogue history, namely response pairs,sequence augmentation pairs, and user pairs. We pre-train the utterance encoderand the history encoder towards the contrastive objectives and use thesepre-trained encoders for generating user profiles while personalized responsegeneration. Experimental results on two real-world datasets show a significantimprovement in our proposed model MCP compared with the existing methods.

MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling