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Cross-Document Event Coreference (CDEC) annotation is challenging and difficult to scale, resulting in existing datasets being small and lacking diversity. We introduce a new approach leveraging large language models (LLMs) to decontextualize event mentions, by simplifying the document-level annotation task to sentence pairs with enriched context, enabling the creation of Richer EventCorefBank (RECB), a denser and more expressive dataset annotated at faster speed. Decontextualization has been shown to improve annotation speed without compromising quality and to enhance model performance. Our baseline experiment indicates that systems trained on RECB achieve comparable results on the EventCorefBank(ECB+) test set, showing the high quality of our dataset and its generalizability on other CDEC datasets. In addition, our evaluation shows that the strong baseline models are still struggling with RECB comparing to other CDEC datasets, suggesting that the richness and diversity of RECB present significant challenges to current CDEC systems. 

Uniform Meaning Representation (UMR) is the next phase of semantic formalism following Abstract Meaning Representation (AMR), with added focus on inter-sentential relations allowing the representational scope of UMR to cover a full document. This, in turn, greatly increases the complexity of its parsing task with the additional requirement of capturing document-level linguistic phenomena such as coreference, modal and temporal dependencies. In order to establish a strong baseline despite the small size of recently released UMR v1.0 corpus, we introduce a pipeline model that does not require any training. At the core of our method is a two-track strategy of obtaining UMR’s sentence and document graphs separately, with the document-level triples being compiled at the token level and the sentence graph being converted from AMR graphs. By leveraging alignment between AMR and its sentence, we are able to generate the first automatic English UMR parses. 

In this paper, we present AnCast, an intuitive and efficient tool for evaluating graph-based meaning representations (MR). AnCast implements evaluation metrics that are well understood in the NLP community, and they include concept F1, unlabeled relation F1, labeled relation F1, and weighted relation F1. The efficiency of the tool comes from a novel anchor broadcast alignment algorithm that is not subject to the trappings of local maxima. We show through experimental results that the AnCast score is highly correlated with the widely used Smatch score, but its computation takes only about 40% the time. 

In this paper, we present AnCast, an intuitive and efficient tool for evaluating graph-based meaning representations (MR). AnCast implements evaluation metrics that are well understood in the NLP community, and they include concept F1, unlabeled relation F1, labeled relation F1, and weighted relation F1. The efficiency of the tool comes from a novel anchor broadcast alignment algorithm that is not subject to the trappings of local maxima. We show through experimental results that the AnCast score is highly correlated with the widely used Smatch score, but its computation takes only about 40% the time. 

We explore using LLMs, GPT-4 specifically, to generate draft sentence-level Chinese Uniform Meaning Representations (UMRs) that human annotators can revise to speed up the UMR annotation process. In this study, we use few-shot learning and Think-Aloud prompting to guide GPT-4 to generate sentence-level graphs of UMR. Our experimental results show that compared with annotating UMRs from scratch, using LLMs as a preprocessing step reduces the annotation time by two thirds on average. This indicates that there is great potential for integrating LLMs into the pipeline for complicated semantic annotation tasks. 

We explore using LLMs, GPT-4 specifically, to generate draft sentence-level Chinese Uniform Meaning Representations (UMRs) that human annotators can revise to speed up the UMR annotation process. In this study, we use few-shot learning and Think-Aloud prompting to guide GPT-4 to generate sentence-level graphs of UMR. Our experimental results show that compared with annotating UMRs from scratch, using LLMs as a preprocessing step reduces the annotation time by two thirds on average. This indicates that there is great potential for integrating LLMs into the pipeline for complicated semantic annotation tasks. 

This paper explores utilizing Large Language Models (LLMs) to perform Cross-Document Event Coreference Resolution (CDEC) annotations and evaluates how they fare against human annotators with different levels of training. Specifically, we formulate CDEC as a multi-category classification problem on pairs of events that are represented as decontextualized sentences, and compare the predictions of GPT-4 with the judgment of fully trained annotators and crowdworkers on the same data set. Our study indicates that GPT-4 with zero-shot learning outperformed crowd-workers by a large margin and exhibits a level of performance comparable to trained annotators. Upon closer analysis, GPT-4 also exhibits tendencies of being overly confident, and force annotation decisions even when such decisions are not warranted due to insufficient information. Our results have implications on how to perform complicated annotations such as CDEC in the age of LLMs, and show that the best way to acquire such annotations might be to combine the strengths of LLMs and trained human annotators in the annotation process, and using untrained or undertrained crowdworkers is no longer a viable option to acquire high-quality data to advance the state of the art for such problems. 

UMR-Writer is a web-based tool for annotating semantic graphs with the Uniform Meaning Representation (UMR) scheme. UMR is a graph-based semantic representation that can be applied cross-linguistically for deep semantic analysis of texts. In this work, we implemented a new keyboard interface in UMR-Writer 2.0, which is a powerful addition to the original mouse interface, supporting faster annotation for more experienced annotators. The new interface also addresses issues with the original mouse interface. Additionally, we demonstrate an efficient workflow for annotation project management in UMR-Writer 2.0, which has been applied to many projects.