Despite progress in automated fact-checking, most systems require a
significant amount of labeled training data, which is expensive. In this paper,
we propose a novel zero-shot method, which instead of operating directly on the
claim and evidence sentences, decomposes them into semantic triples augmented
using external knowledge graphs, and uses large language models trained for
natural language inference. This allows it to generalize to adversarial
datasets and domains that supervised models require specific training data for.
Our empirical results show that our approach outperforms previous zero-shot
approaches on FEVER, FEVER-Symmetric, FEVER 2.0, and Climate-FEVER, while being
comparable or better than supervised models on the adversarial and the
out-of-domain datasets.

本文提出了一种新的零射击方法，将声称和证据句子转化为语义三元组并使用大型语言模型进行自然语言推理，从而在没有特定训练数据的敌对数据集和领域中广义推广，在 FEVER、FEVER-Symmetric、FEVER 2.0 和 Climate-FEVER 数据集上优于先前的零射击方法，同时在敌对和异域数据集上与监督模型相当甚至更好。

基于语义三元组和知识图谱的零 - shot 事实检查

Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs

Recommender systems have found significant commercial success but still
struggle with integrating new users. Since users often interact with content in
different domains, it is possible to leverage a user's interactions in previous
domains to improve that user's recommendations in a new one (multi-domain
recommendation). A separate research thread on knowledge graph enhancement uses
external knowledge graphs to improve single domain recommendations (knowledge
graph enhancement). Both research threads incorporate related information to
improve predictions in a new domain. We propose in this work to unify these
approaches: Using information from interactions in other domains as well as
external knowledge graphs to make predictions in a new domain that would be
impossible with either information source alone. We apply these ideas to a
dataset derived from millions of users' requests for content across three
domains (videos, music, and books) in a live virtual assistant application. We
demonstrate the advantage of combining knowledge graph enhancement with
previous multi-domain recommendation techniques to provide better overall
recommendations as well as for better recommendations on new users of a domain.

本文提出了一种利用用户在其他领域的交互信息以及外部知识图谱的思想来改进新领域推荐的方法，并在虚拟助手应用程序中的三个领域（视频、音乐和图书）的大量用户请求的数据集上证明了该方法的优势。

AI 助手应用中的零样本和多领域推荐的多任务知识增强

Multi-Task Knowledge Enhancement for Zero-Shot and Multi-Domain  Recommendation in an AI Assistant Application

The chit-chat-based conversational recommendation systems (CRS) provide item
recommendations to users through natural language interactions. To better
understand user's intentions, external knowledge graphs (KG) have been
introduced into chit-chat-based CRS. However, existing chit-chat-based CRS
usually generate repetitive item recommendations, and they cannot properly
infuse knowledge from KG into CRS to generate informative responses. To remedy
these issues, we first reformulate the conversational recommendation task to
highlight that the recommended items should be new and possibly interested by
users. Then, we propose the Knowledge-Enriched Conversational Recommendation
System (KECRS). Specifically, we develop the Bag-of-Entity (BOE) loss and the
infusion loss to better integrate KG with CRS for generating more diverse and
informative responses. BOE loss provides an additional supervision signal to
guide CRS to learn from both human-written utterances and KG. Infusion loss
bridges the gap between the word embeddings and entity embeddings by minimizing
distances of the same words in these two embeddings. Moreover, we facilitate
our study by constructing a high-quality KG, \ie The Movie Domain Knowledge
Graph (TMDKG). Experimental results on a large-scale dataset demonstrate that
KECRS outperforms state-of-the-art chit-chat-based CRS, in terms of both
recommendation accuracy and response generation quality.

该论文提出了基于知识图谱的对话推荐系统（KECRS），通过引入 Bag-of-Entity loss 和 infusion loss，将外部知识图谱和人类语言相结合，从而生成更多元化和信息丰富的响应建议。实验表明，KECRS 的准确性和生成质量在推荐系统方面均优于现有的基于聊天的对话推荐系统。