Prior works formulate the extraction of event-specific arguments as a span extraction problem, where event arguments are explicit --- i.e. assumed to be contiguous spans of text in a document. In this study, we revisit this definition of Event Extraction (EE) by introducing two key argument types that cannot be modeled by existing EE frameworks. First, implicit arguments are event arguments which are not explicitly mentioned in the text, but can be inferred through context. Second, scattered arguments are event arguments that are composed of information scattered throughout the text. These two argument types are crucial to elicit the full breadth of information required for proper event modeling.

To support the extraction of explicit, implicit, and scattered arguments, we develop a novel dataset, DiscourseEE, which includes 7,464 argument annotations from online health discourse. Notably, 51.2% of the arguments are implicit, and 17.4% are scattered, making DiscourseEE a unique corpus for complex event extraction. Additionally, we formulate argument extraction as a text generation problem to facilitate the extraction of complex argument types. We provide a comprehensive evaluation of state-of-the-art models and highlight critical open challenges in generative event extraction. Our data and codebase are available at https://omar-sharif03.github.io/DiscourseEE.

Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments

Amidst the sharp rise in the evaluation of large language models (LLMs) on various tasks, we find that semantic textual similarity (STS) has been under-explored. In this study, we show that STS can be cast as a text generation problem while maintaining strong performance on multiple STS benchmarks. Additionally, we show generative LLMs significantly outperform existing encoder-based STS models when characterizing the semantic similarity between two texts with complex semantic relationships dependent on world knowledge. We validate this claim by evaluating both generative LLMs and existing encoder-based STS models on three newly-collected STS challenge sets which require world knowledge in the domains of Health, Politics, and Sports. All newly-collected data is sourced from social media content posted after May 2023 to ensure the performance of closed-source models like ChatGPT cannot be credited to memorization. Our results show that, on average, generative LLMs outperform the best encoder-only baselines by an average of 22.3% on STS tasks requiring world knowledge. Our results suggest generative language models with STS-specific prompting strategies achieve state-of-the-art performance in complex, domain-specific STS tasks.

Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity

Information Extraction

technical paper

## Welcome to EMNLP 2024! 
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

*This is an IN-PERSON ONLY workshop. Session recording will be available here.* **Workshop Organizers:** Sebastian Gehrmann, Khyathi Raghavi Chandu, Alex Wang, Elizabeth Clark, Kaustubh Dhole and João Sedoc
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*Coffee Break 3:30 pm* [**Workshop Website**](https://gem-benchmark.com/workshop) Many new NLP applications are cast through the lens of natural language generation. With the advent of these new approaches, many opportunities arise: generation in previously less studied languages, new evaluation paradigms, methods for corpus creation, more efficient architectures, strategies for safe deployments, among many others. At the same time, we can learn from the rich history of NLG research to further improve generation methods. These developments require robust and sound NLG evaluation processes. To that end, the GEM workshop aims to encourage the development of model auditing and human evaluation strategies, and to popularize model evaluations in languages beyond English. [**Schedule**](https://gem-benchmark.com/workshop)
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W11: Natural Language Generation, Evaluation, and Metric (GEM)

workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

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EMNLP 2023 General Chair

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Joseph Gatto

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Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments

Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity

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