United States

Information extraction has led the way for event detection from text for many years. Recent advances in neural models, such as Large Language Models (LLMs) and Vision-Language Models (VLMs), have enabled the integration of multiple modalities, providing richer sources of information about events. Concurrently, the development of schema graphs and 3D reconstruction methods has enhanced our ability to visualize and annotate complex events. Building on these innovations, we introduce the MUMOSA (MUlti-MOdal Situation Awareness) interactive dashboard that brings these diverse resources together. MUMOSA aims to provide a comprehensive platform for event situational awareness, offering users a powerful tool for understanding and analyzing complex scenarios across modalities.

EMNLP 2024

MUMOSA, Interactive Dashboard for MUlti-MOdal Situation Awareness

3d gaussian splatting

multi-modal information extraction

multi-modal event detection

schemas

information extraction

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

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.

Language models (LMs) trained on web-scale datasets are largely successful due to their ability to memorize large amounts of training data, even if only present in a few examples. These capabilities are often desirable in evaluation on tasks such as question answering but raise questions about whether these models can exhibit genuine reasoning or succeed only at mimicking patterns from the training data. This distinction is particularly salient in forecasting tasks, where the answer is not present in the training data, and the model must reason to make logical deductions. We present Reasoning and Tools for Forecasting (RTF), a framework of reasoning-and-acting (ReAct) agents that can dynamically retrieve updated information and run numerical simulation with equipped tools. We evaluate our model with questions from competitive forecasting platforms and demonstrate that our method is competitive with and can outperform human predictions. This suggests that LMs, with the right tools, can indeed think and adapt like humans, offering valuable insights for real-world decision-making.

Reasoning and Tools for Human-Level Forecasting

Document-level information extraction (doc-IE) plays a pivotal role in the realm of natural language processing (NLP). This paper embarks on a comprehensive review and discussion of contemporary literature related to doc-IE. In addition, we conduct a thorough error analysis using state-of-the-art algorithms, shedding light on their limitations and remaining challenges for tackling the task of doc-IE. Our findings demonstrate that issues like entity coreference resolution and the lack of robust reasoning significantly hinder the effectiveness of document-level information extraction (doc-IE). Additionally, we uncover new challenges, including labeling noise and relation transitivity. The overarching objective of this survey paper is to provide valuable insights that can empower NLP researchers to further advance the performance of doc-IE.

A Comprehensive Survey on Document-Level Information Extraction

This paper aims to map out the current landscape of generative approaches to the task of event extraction. In surveying the emerging literature on the topic, we identify the distinctive properties of existing studies and catalogue them to build a comprehensive view of the various techniques employed. Finally, looking ahead, we argue for a new generative formulation of event extraction, allowing for a better fit between methodology and task - a proposal that could also pertain to many other traditional NLP tasks currently based on annotations of text-spans.

Generative Approaches to Event Extraction: Survey and Outlook

We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our approach uses a high-quality curated dataset of time and location annotations in a corpus of epidemiology papers to train an encoder-decoder architecture. We also explored the use of data augmentation techniques during training. Our findings suggest that a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurate predict the relevant scenario information of a particular entity or event.

When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context

Identifying events from text has a long past in narrative analysis, but a short history in Natural Language Processing (NLP). In this position paper, a question is asked: given the telling of a sequence of real-world events by a news narrator, what do NLP event extraction models capture, and what do they miss? Insights from critical discourse analysis (CDA) and from a series of movements in literary criticism motivate us to model the narrated logic in news narratives. As a result, a computational framework is proposed to model the function of news narration, which shapes the narrated world, consumed by news narratees. As a simplification, we represent the causal logic between events depicted in the narrated world.

Narration as Functions: from Events to Narratives

Automatic storytelling is a difficult NLP task that poses a challenge even for state-of-the-art large language models. This paper proposes a pipeline for interactive fairy tale generation in a mixed-initiative setting. Our approach introduces a story goal as a stopping condition, imposes minimal structure on the narrative in the form of a simple emotional arc, and controls the transition between the stages of the story via system prompt engineering. The resulting framework reconciles creating a structured and complete short-form narrative with retaining player agency and allowing users to influence the storyline through their input. We evaluate our approach with several proprietary and open-source language models and examine its transferability to different languages, specifically English and Russian.

How to tame your plotline: A framework for goal-driven interactive fairy tale generation

This study explores the reception and understanding of the transmedia ensemble surrounding Bill Willingham's Fables (2002-2015), a comic series reimagining fairytale characters in a modern setting. Fables expands its narrative across multiple media, including spin-off comics, a novel, and the video game The Wolf Among Us. This research investigates key questions: Can we identify a distinct group of transmedia consumers? What elements of the narrative sustain interest across media? A survey of 58 participants reveals that while most enter the franchise through the comic series, a significant number are introduced via the video game. The findings indicate that Fables fans are highly engaged transmedia consumers, with a majority exploring several parts of the franchise wanting to pursue narrative exploration. This study offers insights into how transmedia narratives are consumed, emphasizing the role of familiar story elements in encouraging cross-media engagement.

Understanding Transmedia Storytelling: Reception and Narrative Comprehension in Bill Willingham's Fables Franchise

In this study, we explore the application of large language models (LLMs) to analyze narrative discourse within the framework established by the field of narratology. We develop a set of elementary narrative features derived from prior theoretical work that focus on core dimensions of narrative, including time, setting, and perspective. Through experiments with GPT-4 and fine-tuned open-source models like Llama3, we demonstrate the models' ability to annotate narrative passages with reasonable levels of agreement with human annotators. Leveraging a dataset of human-annotated passages spanning 18 distinct narrative and non-narrative genres, our work provides empirical support for the deictic theory of narrative communication. This theory posits that a fundamental function of storytelling is the focalization of attention on distant human experiences to facilitate social coordination. We conclude with a discussion of the possibilities for LLM-driven narrative discourse understanding.

Using Large Language Models for Understanding Narrative Discourse

Narrative is widely recognized as a powerful tool for structuring information and facilitating comprehension of complex ideas in various domains, such as science communication. This paper investigates whether incorporating narrative elements can assist Large Language Models (LLMs) in solving complex tasks more effectively. We propose a novel approach, Story of Thought (SoT), integrating narrative structures into prompting techniques for problem-solving tasks. This approach involves constructing narratives around problem statements and creating a framework to identify and organize relevant information. We hypothesize that this narrative-based information curation process enhances problem comprehension by contextualizing critical information and highlighting causal relationships within the problem space. Our experimental results show that the SoT approach consistently surpasses Chain of Thought (CoT) and Analogical Reasoning in GPQA tasks, achieving higher accuracy and better solutions in physics, chemistry, and biology problem-solving tasks with all tested OpenAI, Meta, and Mistral LLMs.

Can Stories Help LLMs Reason? Curating Information Space Through Narrative

Stories are a rich representation of the beliefs, practices, and languages of communities, and a source of cultural information for researchers in the social sciences and humanities. Characters – their personalities and behaviour – are a key aspect of this process. Large Language Models (LLMs) are becoming increasingly popular tools for creative text generation. Our work creates the GPT-Writing Prompts dataset, which pairs 97,219 prompts and human-written stories from the Reddit WritingPrompts dataset with corresponding GPT-3.5 generated stories. We quantify the emotional and descriptive features of protagonists in stories along six lexical dimensions: appearance, intellect, power, valence, arousal, dominance. We also quantify biases in character portrayal based on protagonist gender in both human-written and GPT-generated stories.

Downloads

Next from EMNLP 2024

Reasoning and Tools for Human-Level Forecasting

Similar lecture

LLaVA-MS-PIT: Multi-Modal Schema-Guided Progressive Instruction Tuning for Multi-Modal Event Extraction

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from EMNLP 2024

Reasoning and Tools for Human-Level Forecasting

Similar lecture

LLaVA-MS-PIT: Multi-Modal Schema-Guided Progressive Instruction Tuning for Multi-Modal Event Extraction

Downloads