United States

Decision making theories such as Fuzzy-Trace Theory (FTT) suggest that individuals tend to rely on gist, or bottom-line meaning, in the text when making decisions. In this work, we delineate the process of developing GisPy, an open-source tool in Python for measuring the Gist Inference Score (GIS) in text. Evaluation of GisPy on documents in three benchmarks from the news and scientific text domains demonstrates that scores generated by our tool significantly distinguish low vs. high gist documents. Our tool is publicly available to use at: https://github.com/phosseini/GisPy.

NAACL 2022

GisPy: A Tool for Measuring Gist Inference Score in Text

coh-metrix

gist

open-source

decision making

coherence

## Welcome to EMNLP 2024! 
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EMNLP 2024

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workshop paper

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 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 and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc.
While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.

''Let Your Characters Tell Their Story'': A Dataset for Character-Centric Narrative Understanding

Story characters not only perform actions, they typically also perceive, feel, think, and communicate. Here we are interested in how children render characters’ perspectives when freely telling a fantasy story. Drawing on a sample of 150 narratives elicited from Dutch children aged 4-12, we provide an inventory of 750 instances of character-perspective representation (CPR), distinguishing fourteen different types. Firstly, we observe that character perspectives are ubiquitous in freely told children’s stories and take more varied forms than traditional frameworks can accommodate. Secondly, we discuss variation in the use of different types of CPR across age groups, finding that character perspectives are being fleshed out in more advanced and diverse ways as children grow older. Thirdly, we explore whether such variation can be meaningfully linked to automatically extracted linguistic features, thereby probing the potential for using automated tools from NLP to extract and classify character perspectives in children’s stories.

Looking from the Inside: How Children Render Character’s Perspectives in Freely Told Fantasy Stories

This paper shows how to use large-scale pre-trained language models to extract character roles from narrative texts without training data. Queried with a zero-shot question-answering prompt, GPT-3 can identify the hero, villain, and victim in diverse domains: newspaper articles, movie plot summaries, and political speeches.

Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data

We present our project proposal for extracting biomedical information from German clinical narratives with limited amounts of annotations. We first describe the applied strategies in transfer learning and active learning for solving our problem. After that, we discuss the design of the user interface for both supplying model inspection and obtaining user annotations in the interactive environment.

Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop

An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one that does not scale well. Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision. 
We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour to quickly build or extend a taxonomy to better fit information needs.

Taxonomy Builder: A Data-driven and User-centric Tool for Streamlining Taxonomy Construction

Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited. We present work-in-progress on adapting an IC system to integrate human feedback, with the goal to make it easily adaptable to user-specific data. Our approach builds on a base IC model pre-trained on the MS COCO dataset, which generates captions for unseen images. The user will then be able to offer feedback on the image and the generated/predicted caption, which will be augmented to create additional training instances for the adaptation of the model. The additional instances are integrated into the model using step-wise updates, and a sparse memory replay component is used to avoid catastrophic forgetting. We hope that this approach, while leading to improved results, will also result in customizable IC models.

Putting Humans in the Image Captioning Loop

With the growth of Automatic Content Moderation (ACM) on widely used social media platforms, transparency into the design of moderation technology and policy is necessary for online communities to advocate for themselves when harms occur. In this work, we describe a suite of interactive modules to support the exploration of various aspects of this technology, and particularly of those components that rely on English models and datasets for hate speech detection, a subtask within ACM. We intend for this demo to support the various stakeholders of ACM in investigating the definitions and decisions that underpin current technologies such that those with technical knowledge and those with contextual knowledge may both better understand existing systems.

An Interactive Exploratory Tool for the Task of Hate Speech Detection

As digital social platforms and mobile technologies become more prevalent and robust, the use of Artificial Intelligence (AI) in facilitating human communication will grow. This, in turn, will encourage development of intuitive, adaptive, and effective empathic AI interfaces that better address the needs of socially and culturally diverse communities. In this paper, we present several design considerations of an intelligent digital interface intended to guide the clinicians toward more empathetic communication. This approach allows various communities of practice to investigate how AI, on one side, and human communication and healthcare needs, on the other, can contribute to each other’s development.

Design Considerations for an NLP-Driven Empathy and Emotion Interface for Clinician Training via Telemedicine

This paper proposes an approach to the design of an ethical human-AI reasoning support system for decision makers in refugee law. In the context of refugee status determination, practitioners mostly rely on text data. We therefore investigate human-AI cooperation in legal natural language processing. Specifically, we want to determine which design methods can be transposed to legal text analytics. Although little work has been done so far on human-centered design methods applicable to the legal domain, we assume that introducing iterative cooperation and user engagement in the design process is (1) a method to reduce technical limitations of an NLP system and (2) that it will help design more ethical and effective applications by taking users’ preferences and feedback into account. The proposed methodology is based on three main design steps: cognitive process formalization in models understandable by both humans and computers, speculative design of prototypes, and semi-directed interviews with a sample of potential users.

Human-Centered Computing in Legal NLP: An Application to Refugee Status Determination

With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes research human-centric? We address this question by providing a working definition and define how a research pipeline can be split into different stages in which human-centric components can be added. Additionally, we discuss existing NLP with HCR components and define a series of guiding questions, which can serve as starting points for researchers interested in exploring human-centric research approaches. We hope that this work would inspire researchers to refine the proposed definition and to pose other questions that might be meaningful for achieving HCR.

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''Let Your Characters Tell Their Story'': A Dataset for Character-Centric Narrative Understanding

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