Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes

• URL: http://arxiv.org/abs/2210.12197v1
• Date: Fri, 21 Oct 2022 18:54:17 GMT
• Title: Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes
• Authors: Oren Sultan, Dafna Shahaf
• Abstract summary: Much research has suggested that analogies are key to non-brittle systems that can adapt to new domains. Despite their importance, analogies received little attention in the NLP community.
• Score: 12.8252101640812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analogy-making gives rise to reasoning, abstraction, flexible categorization and counterfactual inference -- abilities lacking in even the best AI systems today. Much research has suggested that analogies are key to non-brittle systems that can adapt to new domains. Despite their importance, analogies received little attention in the NLP community, with most research focusing on simple word analogies. Work that tackled more complex analogies relied heavily on manually constructed, hard-to-scale input representations. In this work, we explore a more realistic, challenging setup: our input is a pair of natural language procedural texts, describing a situation or a process (e.g., how the heart works/how a pump works). Our goal is to automatically extract entities and their relations from the text and find a mapping between the different domains based on relational similarity (e.g., blood is mapped to water). We develop an interpretable, scalable algorithm and demonstrate that it identifies the correct mappings 87% of the time for procedural texts and 94% for stories from cognitive-psychology literature. We show it can extract analogies from a large dataset of procedural texts, achieving 79% precision (analogy prevalence in data: 3%). Lastly, we demonstrate that our algorithm is robust to paraphrasing the input texts.

Related papers

• Explaining Text Similarity in Transformer Models [52.571158418102584]
  Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers. We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models. Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
  arXiv  Detail & Related papers  (2024-05-10T17:11:31Z)
• ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies [16.92480305308536]
  We develop a pipeline for creating complex, paragraph-based analogies. We publish a gold-set, validated by humans, and a silver-set, generated automatically. We demonstrate that our silver-set is useful for training models.
  arXiv  Detail & Related papers  (2024-03-02T08:53:40Z)
• FAME: Flexible, Scalable Analogy Mappings Engine [22.464249291871937]
  In this work, we relax the input requirements, requiring only names of entities to be mapped. We automatically extract commonsense representations and use them to identify a mapping between the entities. Our framework can handle partial analogies and suggest new entities to be added.
  arXiv  Detail & Related papers  (2023-11-03T12:08:02Z)
• StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding [72.38872974837462]
  We evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus. textscStory Analogy contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. We observe that the data in textscStory Analogy can improve the quality of analogy generation in large language models.
  arXiv  Detail & Related papers  (2023-10-19T16:29:23Z)
• ARN: Analogical Reasoning on Narratives [13.707344123755126]
  We develop a framework that operationalizes dominant theories of analogy, using narrative elements to create surface and system mappings. We show that while all LLMs can largely recognize near analogies, even the largest ones struggle with far analogies in a zero-shot setting.
  arXiv  Detail & Related papers  (2023-10-02T08:58:29Z)
• Why Do We Need Neuro-symbolic AI to Model Pragmatic Analogies? [6.8107181513711055]
  A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning. We discuss analogies at four distinct levels of complexity: lexical analogies, syntactic analogies, semantic analogies, and pragmatic analogies. We employ Neuro-symbolic AI techniques that combine statistical and symbolic AI, informing the representation of unstructured text to highlight and augment relevant content, provide abstraction and guide the mapping process.
  arXiv  Detail & Related papers  (2023-08-02T21:13:38Z)
• Description-Based Text Similarity [59.552704474862004]
  We identify the need to search for texts based on abstract descriptions of their content. We propose an alternative model that significantly improves when used in standard nearest neighbor search.
  arXiv  Detail & Related papers  (2023-05-21T17:14:31Z)
• ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base [51.777618249271725]
  ANALOGYKB is a million-scale analogy knowledge base derived from existing knowledge graphs (KGs) It identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large language models (LLMs)
  arXiv  Detail & Related papers  (2023-05-10T09:03:01Z)
• Contextualized Semantic Distance between Highly Overlapped Texts [85.1541170468617]
  Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. This paper aims to address the issue with a mask-and-predict strategy. We take the words in the longest common sequence as neighboring words and use masked language modeling (MLM) to predict the distributions on their positions. Experiments on Semantic Textual Similarity show NDD to be more sensitive to various semantic differences, especially on highly overlapped paired texts.
  arXiv  Detail & Related papers  (2021-10-04T03:59:15Z)
• Narrative Incoherence Detection [76.43894977558811]
  We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding. Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow.
  arXiv  Detail & Related papers  (2020-12-21T07:18:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.