Zero-Shot Script Parsing
Script knowledge (shrank, 1977) proved useful to a variety of NLP tasks. However, existing resources only covering a small number of activities, limiting its practical usefulness. In this work, we propose a zero-shot learning approach to script parsing, the task of tagging texts with pre-defined, scenario-specific event and participant types, which makes it possible to acquire script knowledge without domain-specific annotations. We (1) learn representations of potential event and participant mentions by promoting cluster consistency according to the annotated data; (2) perform clustering on the event / participant candidates from unannotated texts that belongs to an unseen scenario. We further exploit dependency and coreference information. The model achieves 68.1/74.4 average F1 for event / participant parsing, respectively, outperforming a previous CRF model that has access to domain-specific supervision.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringZero-Shot LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Zero-shot Script Parsing
Script knowledge is useful to a variety of NLP tasks. However, existing resources only cover a small number of activities, limiting its practical usefulness. In this work, we propose a zero-shot learning approach to scri…
Zero-Shot LearningZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models
We explore the use of large language models (LLMs) for zero-shot semantic parsing. Semantic parsing involves mapping natural language utterances to task-specific meaning representations. Language models are generally tra…
Extractive Question-AnsweringLanguage ModelingLanguage ModellingQuestion Answering+2ChatGPT is a Potential Zero-Shot Dependency Parser
Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance. However, it remains an understudied question whether pre-trained language mod…
Dependency ParsingSEQZERO: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models
Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing.The canonical utterance is often lengthy and complex due to the compositional …
Out-of-Distribution GeneralizationSemantic ParsingSeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models
Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing. The canonical utterance is often lengthy and complex due to the compositional…
Out-of-Distribution GeneralizationSemantic Parsing