An efficient customer service management system hinges on precise forecasting
of service volume. In this scenario, where data non-stationarity is pronounced,
successful forecasting heavily relies on identifying and leveraging similar
historical data rather than merely summarizing periodic patterns. Existing
models based on RNN or Transformer architectures often struggle with this
flexible and effective utilization. To address this challenge, we propose an
efficient and adaptable cross-attention module termed RACA, which effectively
leverages historical segments in forecasting task, and we devised a precise
representation scheme for querying historical sequences, coupled with the
design of a knowledge repository. These critical components collectively form
our Retrieval-Augmented Temporal Sequence Forecasting framework (RATSF). RATSF
not only significantly enhances performance in the context of Fliggy hotel
service volume forecasting but, more crucially, can be seamlessly integrated
into other Transformer-based time-series forecasting models across various
application scenarios. Extensive experimentation has validated the
effectiveness and generalizability of this system design across multiple
diverse contexts.

一种有效的客户服务管理系统依赖于准确预测服务量。为了解决数据非稳态的挑战，本文提出了一种高效且适应性强的跨关注机制模块 RACA，用于预测任务中利用历史段落，并设计了一种准确的历史序列查询表示方案以及知识库的设计。这些关键组件共同构成了检索增强的时间序列预测框架（RATSF），该框架不仅在 Fliggy 酒店服务量预测中显著提升性能，还可以在各种应用场景中无缝集成到其他基于 Transformer 的时间序列预测模型中。大量实验验证了该系统设计在多个不同环境中的有效性和普适性。

RATSF: 通过检索增强的时间序列预测提升客户服务量管理能力

RATSF: Empowering Customer Service Volume Management through  Retrieval-Augmented Time-Series Forecasting

Tackling online hatred using informed textual responses - called counter
narratives - has been brought under the spotlight recently. Accordingly, a
research line has emerged to automatically generate counter narratives in order
to facilitate the direct intervention in the hate discussion and to prevent
hate content from further spreading. Still, current neural approaches tend to
produce generic/repetitive responses and lack grounded and up-to-date evidence
such as facts, statistics, or examples. Moreover, these models can create
plausible but not necessarily true arguments. In this paper we present the
first complete knowledge-bound counter narrative generation pipeline, grounded
in an external knowledge repository that can provide more informative content
to fight online hatred. Together with our approach, we present a series of
experiments that show its feasibility to produce suitable and informative
counter narratives in in-domain and cross-domain settings.

该研究提出一种基于外部知识库的完整反叙事生成管道，以对抗网上仇恨。实验证明，该模型可以在领域内和跨领域环境下生成适合和信息丰富的反叙事。

面向知识驱动的仇恨言论反叙事生成

Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech

One of the key requirements to facilitate semantic analytics of information
regarding contemporary and historical events on the Web, in the news and in
social media is the availability of reference knowledge repositories containing
comprehensive representations of events and temporal relations. Existing
knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata,
focus mostly on entity-centric information and are insufficient in terms of
their coverage and completeness with respect to events and temporal relations.
EventKG presented in this paper is a multilingual event-centric temporal
knowledge graph that addresses this gap. EventKG incorporates over 690 thousand
contemporary and historical events and over 2.3 million temporal relations
extracted from several large-scale knowledge graphs and semi-structured sources
and makes them available through a canonical representation.

本篇研究介绍了一个多语言的以事件为中心的知识图谱 EventKG，其中包括超过 690,000 个当代和历史事件以及超过 2.3 百万的时间关系，该知识图谱填补了现有知识库在事件和时间关系上的不足。

EventKG：一个多语言的以事件为中心的时序知识图谱

EventKG: A Multilingual Event-Centric Temporal Knowledge Graph

Entity linking is an indispensable operation of populating knowledge
repositories for information extraction. It studies on aligning a textual
entity mention to its corresponding disambiguated entry in a knowledge
repository. In this paper, we propose a new paradigm named distantly supervised
entity linking (DSEL), in the sense that the disambiguated entities that belong
to a huge knowledge repository (Freebase) are automatically aligned to the
corresponding descriptive webpages (Wiki pages). In this way, a large scale of
weakly labeled data can be generated without manual annotation and fed to a
classifier for linking more newly discovered entities. Compared with
traditional paradigms based on solo knowledge base, DSEL benefits more via
jointly leveraging the respective advantages of Freebase and Wikipedia.
Specifically, the proposed paradigm facilitates bridging the disambiguated
labels (Freebase) of entities and their textual descriptions (Wikipedia) for
Web-scale entities. Experiments conducted on a dataset of 140,000 items and
60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze
the feature performance and improve the F1-measure to 0.545.

本文提出了一种名为遥感监督实体链接 (DSEL) 的新模式，它利用 Freebase 和 Wikipedia 的各自优势，桥接实体的已消除的标签 (Freebase) 和它们的文本描述 (Wikipedia)，自动对齐大量的弱标注数据，并将其提供给分类器来链接新发现的实体，实验结果表明，在一个由 140,000 个条目和 60,000 个特征组成的数据集上，基线 F1 值为 0.517，而经过改进后可以达到 0.545。