United States

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.

EMNLP 2024

A Comprehensive Survey on Document-Level Information Extraction

document-level information extraction

relation extraction

event extraction

evaluation

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

## 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.

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.

The GPT-WritingPrompts Dataset: A Comparative Analysis of Character Portrayal in Short Stories

Recent advancements in NLP and language modeling have significantly enhanced LLMs’ ability to extract personal identifiers in text beyond the writing style, including names, locations, and cultural references to infer the writer’s identity. This constitutes an attack on privacy of individuals and could have fatal consequences for some personal narratives (e.g. domestic violence). In this work, we investigate domestic violence narratives in a Reddit community (r/do- mesticviolence), with the aim of identifying common patterns across a large collection of these stories. Our ultimate goal is to leverage the shared structure to develop a privacy preserving framework which assesses the safety of sharing a story. We propose a binary classification approach that utilizes distance vectors as features to classify a story as either ”similar to other domestic violence stories” or ”not similar”. This could serve as a first step toward more sophisticated methods for anonymizing the structure of sensitive narratives.

Is It Safe to Tell Your Story? Towards Achieving Privacy for Sensitive Narratives

This work presents the task of Zero-shot Trajectory Mapping, which focuses on the spatial dimension of narratives. The task consists of two parts: (1) creating a ``map'' with all the locations mentioned in a set of texts, and (2) extracting a trajectory from a single testimony and positioning it within the map. Following recent advances in context length capabilities of large language models, we propose a pipeline for this task in a completely unsupervised manner, without requiring any type of labels. We test the pipeline on a set on 402 Holocaust testimonies and present the resulting map and samples of the trajectory. We find that current long-range models succeed in generating meaningful maps and trajectories. We show how the trajectories can be used to align parallel parts of different testimonies. We conclude that LLMs can serve as beneficial tools for Holocaust research and narrative analysis.

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