Reasoning about Structured Story Representations

关于结构化故事表示的推理

• 批准号: EP/W003309/1
• 负责人: Steven Schockaert
• 金额: $ 163.22万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW003309%2F1
• 关键词: Reasoning; about; Structured; Story; Representations

When we read a story as a human, we build up a mental model of what is described. Such mental models are crucial for reading comprehension. They allow us to relate the story to our earlier experiences, to make inferences that require combining information from different sentences, and to interpret ambiguous sentences correctly. Crucially, mental models capture more information than what is literally mentioned in the story. They are representations of the situations that are described, rather than the text itself, and they are constructed by combining the story text with our commonsense understanding of how the world works.The field of Natural Language Processing (NLP) has made rapid progress in the last few years, but the focus has largely been on sentence-level representations. Stories, such as news articles, social media posts or medical case reports, are essentially modelled as collections of sentences. As a result, current systems struggle with the ambiguity of language, since the correct interpretation of a word or sentence can often only be inferred by taking its broader story context into account. They are also severely limited in their ability to solve problems where information from different sentences needs to be combined. As a final example, current systems struggle to identify correspondences between related stories (e.g. different news articles about the same event), especially if they are written from a different perspective.To address these fundamental challenges, we need a method to learn story-level representations that can act as an analogue to mental models. Intuitively, there are two steps involved in learning such story representations: first we need to model what is literally mentioned in the story, and then we need some form of commonsense reasoning to fill in the gaps. In practice, however, these two steps are closely interrelated: interpreting what is mentioned in the story requires a model of the story context, but constructing this model requires an interpretation of what is mentioned. The solution I propose in this fellowship is based on representations called story graphs. These story graphs encode the events that occur, the entities involved, and the relationships that hold between these entities and events. A story can then be viewed as an incomplete specification of a story graph, similar to how a symbolic knowledge base corresponds to an incomplete specification of a possible world. Based on this view, we will rely on (weighted) logical encodings to represent what we know about a given story. These encodings will in particular serve as a compact representation of a ranking over possible story graphs, i.e. a ranking over possible interpretations of the story. To reason about story graphs, I propose an innovative combination of neural networks with systematic reasoning. The key idea is to use focused inference patterns that are encoded as graph neural networks. The predictions of these neural networks will essentially play the same role as rule applications in symbolic AI frameworks. In this way, our method will tightly integrate the generalisation abilities and flexibility of neural networks with the advantages of having a principled and interpretable high-level reasoning process. The proposed framework will allow us to reason about textual information in a principled way. It will lead to significant improvements in NLP tasks where a commonsense understanding is required of the situations that are described, or where information from multiple sentences or documents needs to be combined. It will furthermore enable a step change in applications that directly rely on structured text representations, such as situational understanding, information retrieval systems for the legal, medical and news domains, and tools for inferring business insights from news stories and social media feeds.

当我们作为一个人阅读一个故事时，我们建立了一个所描述的内容的心理模型。这样的心理模型对阅读理解至关重要。它们允许我们将故事与我们以前的经历联系起来，做出需要结合不同句子的信息的推理，并正确地解释歧义句子。至关重要的是，心理模型捕捉到的信息比故事中字面上提到的更多。它们是对所描述的情景的表示，而不是文本本身，它们是通过将故事文本与我们对世界如何运行的常识理解相结合而构建的。自然语言处理(NLP)领域在过去几年中取得了快速进展，但主要集中在句子级别的表示上。新闻文章、社交媒体帖子或医疗病例报告等故事基本上被建模为句子的集合。因此，目前的系统正在努力解决语言的歧义问题，因为对一个单词或句子的正确解释往往只能通过考虑其更广泛的故事背景来推断。他们在解决来自不同句子的信息需要组合的问题方面的能力也受到严重限制。作为最后一个例子，当前的系统很难识别相关故事(例如，关于同一事件的不同新闻文章)之间的对应关系，特别是如果它们是从不同的角度撰写的。为了解决这些基本挑战，我们需要一种方法来学习故事级别的表征，这种表征可以类似于心理模型。直观地说，学习这样的故事表达涉及两个步骤：首先，我们需要对故事中字面上提到的东西进行建模，然后我们需要某种形式的常识推理来填补空白。然而，在实践中，这两个步骤是密切相关的：解释故事中提到的内容需要故事背景的模型，但构建这个模型需要对提到的内容进行解释。我在这个团契中提出的解决方案是基于被称为故事图的表示法。这些故事图对发生的事件、涉及的实体以及这些实体和事件之间的关系进行编码。然后，故事可以被视为故事图的不完整规范，类似于符号知识库如何对应于对可能世界的不完整规范。基于这个观点，我们将依靠(加权的)逻辑编码来表示我们对给定故事的了解。这些编码将特别用作对可能的故事图的排名的紧凑表示，即对故事的可能解释的排名。为了对故事图进行推理，我提出了一种神经网络与系统推理的创新组合。其关键思想是使用编码为图神经网络的集中推理模式。这些神经网络的预测本质上将发挥与符号人工智能框架中的规则应用相同的作用。这样，我们的方法将把神经网络的泛化能力和灵活性与具有原则性和可解释性的高级推理过程的优势紧密地结合在一起。拟议的框架将使我们能够以原则性的方式对文本信息进行推理。这将大大改善自然语言处理任务，在这些任务中，需要对所描述的情况进行常识理解，或者需要将来自多个句子或文件的信息组合在一起。它还将进一步改变直接依赖结构化文本表示的应用程序，例如情况理解、法律、医疗和新闻领域的信息检索系统，以及从新闻故事和社交媒体订阅源推断商业洞察力的工具。

项目成果

RelBERT: Embedding Relations with Language Models

• DOI: 10.48550/arxiv.2310.00299
• 发表时间: 2023-09
• 期刊: ArXiv
• 作者: Asahi Ushio;José Camacho-Collados;Steven Schockaert

Embeddings as epistemic states: Limitations on the use of pooling operators for accumulating knowledge

作为认知状态的嵌入：使用池算子来积累知识的限制

• DOI: 10.1016/j.ijar.2023.108981
• 发表时间: 2023
• 期刊: International Journal of Approximate Reasoning
• 影响因子: 3.9
• 作者: Schockaert S

Solving Hard Analogy Questions with Relation Embedding Chains

使用关系嵌入链解决困难类比问题

• DOI: 10.18653/v1/2023.emnlp-main.382
• 发表时间: 2023
• 作者: Kumar N