Large pre-trained language models (LMs) have demonstrated impressive
capabilities in generating long, fluent text; however, there is little to no
analysis on their ability to maintain entity coherence and consistency. In this
work, we focus on the end task of narrative generation and systematically
analyse the long-range entity coherence and consistency in generated stories.
First, we propose a set of automatic metrics for measuring model performance in
terms of entity usage. Given these metrics, we quantify the limitations of
current LMs. Next, we propose augmenting a pre-trained LM with a dynamic entity
memory in an end-to-end manner by using an auxiliary entity-related loss for
guiding the reads and writes to the memory. We demonstrate that the dynamic
entity memory increases entity coherence according to both automatic and human
judgment and helps preserving entity-related information especially in settings
with a limited context window. Finally, we also validate that our automatic
metrics are correlated with human ratings and serve as a good indicator of the
quality of generated stories.

本文系统地研究了预训练语言模型在故事生成任务中维持实体连贯性和一致性方面的能力，并提出了动态实体记忆的增强方法，旨在使用辅助实体相关性损失来指导读写实体记忆，以提高实体连贯性和一致性，同时还验证了自动评价指标与人工评价的相似度，从而提出的自动评价指标可以作为生成故事质量的良好指标。

叙事生成中实体使用的连贯和一致性探索

Towards Coherent and Consistent Use of Entities in Narrative Generation

This report describes Athena, a dialogue system for spoken conversation on
popular topics and current events. We develop a flexible topic-agnostic
approach to dialogue management that dynamically configures dialogue based on
general principles of entity and topic coherence. Athena's dialogue manager
uses a contract-based method where discourse constraints are dispatched to
clusters of response generators. This allows Athena to procure responses from
dynamic sources, such as knowledge graph traversals and feature-based
on-the-fly response retrieval methods. After describing the dialogue system
architecture, we perform an analysis of conversations that Athena participated
in during the 2019 Alexa Prize Competition. We conclude with a report on
several user studies we carried out to better understand how individual user
characteristics affect system ratings.

Athena 是一个用于流行话题和时事的口语对话系统，采用基于实体和主题相干性的灵活的主题不可知的对话管理方法，使用基于合同的方法，使对话系统可以从动态源获得响应，并进行了几项用户研究以更好地了解个人用户特征如何影响系统的评级。

Athena：利用话语限制动态构建对话

Athena: Constructing Dialogues Dynamically with Discourse Constraints

The World Wide Web has become a popular source for gathering information and
news. Multimodal information, e.g., enriching text with photos, is typically
used to convey the news more effectively or to attract attention. Photo content
can range from decorative, depict additional important information, or can even
contain misleading information. Therefore, automatic approaches to quantify
cross-modal consistency of entity representation can support human assessors to
evaluate the overall multimodal message, for instance, with regard to bias or
sentiment. In some cases such measures could give hints to detect fake news,
which is an increasingly important topic in today's society. In this paper, we
introduce a novel task of cross-modal consistency verification in real-world
news and present a multimodal approach to quantify the entity coherence between
image and text. Named entity linking is applied to extract persons, locations,
and events from news texts. Several measures are suggested to calculate
cross-modal similarity for these entities using state of the art approaches. In
contrast to previous work, our system automatically gathers example data from
the Web and is applicable to real-world news. Results on two novel datasets
that cover different languages, topics, and domains demonstrate the feasibility
of our approach. Datasets and code are publicly available to foster research
towards this new direction.

该研究论文介绍了一种用于跨媒体实体一致性验证的新方法，并应用于真实世界新闻中，旨在支持人类评估多媒体信息的整体消息，特别是在偏见或情感方面。该方法通过使用最先进的方法和几种不同措施计算跨媒体相似性，其中包括使用命名实体链接从新闻文本中提取人物，位置和事件。该论文的数据集和代码均公开，并展示了该方法在不同语言，主题和领域的两个新数据集上的可行性。