United States

NAACL 2025

The past and future of grapheme-to-phoneme conversion

## 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/)

NAACL 2022

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

invited talk

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Appraisal theories suggest that emotions arise from subjective evaluations of events, referred to as appraisals. The taxonomy of appraisals is quite diverse, and they are usually given ratings on a Likert scale to be annotated in an experiencer-annotator or reader-annotator paradigm. This paper studies GPT-4 as a reader-annotator of 21 specific appraisal ratings in different prompt settings, aiming to evaluate and improve its performance compared to human annotators. We found that GPT-4 is an effective reader-annotator that performs close to or even slightly better than human annotators, and its results can be significantly improved by using a majority voting of five completions. GPT-4 also effectively predicts appraisal ratings and emotion labels using a single prompt, but adding instruction complexity results in poorer performance. We also found that longer event descriptions lead to more accurate annotations for both model and human annotator ratings. This work contributes to the growing usage of LLMs in psychology and the strategies for improving GPT-4 performance in annotating appraisals.

Assessing the Reliability and Validity of GPT-4 in Annotating Emotion Appraisal Ratings

Psychodynamic conflicts are persistent, often unconscious themes that shape a person’s behaviour and experiences. Accurate diagnosis of psychodynamic conflicts is crucial for effective patient treatment and is commonly done via long, manually scored semi-structured interviews. Existing automated solutions for psychiatric diagnosis tend to focus on the recognition of broad disorder categories such as depression, and it is unclear to what extent psychodynamic conflicts which even the patient themselves may not have conscious access to could be automatically recognised from conversation. In this paper, we propose AutoPsyC, the first method for recognising the presence and significance of psychodynamic conflicts from full-length Operationalized Psychodynamic Diagnostics (OPD) interviews using Large Language Models (LLMs). Our approach combines recent advances in parameter-efficient fine-tuning and Retrieval-Augmented Generation (RAG) with a summarisation strategy to effectively process entire 90 minute long conversations. In evaluations on a dataset of 141 diagnostic interviews we show that AutoPsyC consistently outperforms all baselines and ablation conditions on the recognition of four highly relevant psychodynamic conflicts.

AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models

The increasing demand for mental health services has highlighted the need for innovative solutions, particularly in the realm of psychological conversational AI, where the availability of sensitive data is scarce. In this work, we explored the development of a system tailored for mental health support with a novel approach to psychological assessment based on explainable emotional profiles in combination with empathetic conversational models, offering a promising tool for augmenting traditional care, particularly where immediate expertise is unavailable. Our work can be divided into two main parts, intrinsecaly connected to each other. First, we present RACLETTE, a conversational system that demonstrates superior emotional accuracy compared to considered benchmarks in both understanding users’ emotional states and generating empathetic responses during conversations, while progressively building an emotional profile of the user through their interactions. Second, we show how the emotional profiles of a user can be used as interpretable markers for mental health assessment. These profiles can be compared with characteristic emotional patterns associated with different mental disorders, providing a novel approach to preliminary screening and support.

The Emotional Spectrum of LLMs: Leveraging Empathy and Emotion-Based Markers for Mental Health Support

Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves determining the depression severity of a person through self-reported questionnaires or interviews conducted by clinicians. This often leads to delayed treatment and involves substantial human resources. Thus, several works try to automate the process using multimodal data. However, they usually overlook the following: i) The variable contribution of each modality for each question in the questionnaire and ii) Using ordinal classification for the task. This results in sub-optimal fusion and training methods. In this work, we propose a novel Question-wise Modality Fusion (QuestMF) framework trained with a novel Imbalanced Ordinal Log-Loss (ImbOLL) function to tackle these issues. The performance of our framework is comparable to the current state-of-the-art models on the E-DAIC dataset and enhances interpretability by predicting scores for each question. This will help clinicians identify an individual’s symptoms, allowing them to customise their interventions accordingly. We also make the code for the QuestMF framework publicly available.

Enhancing Depression Detection via Question-wise Modality Fusion

Recent work has suggested detection of cognitive distortions as an impactful task for NLP in the clinical space, but the connection between language-detected distortions and validated mental health outcomes has been elusive. In this work, we evaluate the co-occurrence of (a) 10 distortions derived from language-based detectors trained over two common distortion datasets with (b) 12 mental health outcomes contained within two new language-to-mental-health datasets: DS4UD and iHiTOP. We find higher rates of distortions for those with greater mental health condition severity (ranging from r = 0.16 for thought disorders to r = 0.46 for depressed mood), and that the specific distortions of should statements and fortune telling were associated with a depressed mood and being emotionally drained, respectively. This suggested that language-based assessments of cognitive distortion could play a significant role in detection and monitoring of mental health conditions.

Linking Language-based Distortion Detection to Mental Health Outcomes

Computational mental health research develops models to predict and understand psychological phenomena, but often relies on inappropriate measures of psychopathology constructs, undermining validity. We identify three key issues: (1) reliance on unvalidated measures (e.g., self-declared diagnosis) over validated ones (e.g., diagnosis by clinician); (2) treating mental health constructs as categorical rather than dimensional; and (3) focusing on disorder-specific constructs instead of transdiagnostic ones. We outline the benefits of using validated, dimensional, and transdiagnostic measures and offer practical recommendations for practitioners. Using valid measures that reflect the nature and structure of psychopathology is essential for computational mental health research.

Measuring Mental Health Variables in Computational Research: Toward Validated, Dimensional, and Transdiagnostic Approaches

A rigorous psychometric approach is crucial for the accurate measurement of mind-reading abilities. Traditional scoring methods for such tests, which involve lengthy free-text responses, require considerable time and human effort. This study investigates the use of large language models (LLMs) to automate the scoring of psychometric tests. Data were collected from participants aged 13 to 30 years and scored by trained human coders to establish a benchmark. We evaluated multiple LLMs against human assessments, exploring various prompting strate- gies to optimize performance and fine-tuning the models using a subset of the collected data to enhance accuracy. Our results demonstrate that LLMs can assess advanced mind-reading abilities with over 90% accuracy on average. Notably, in most test items, the LLMs achieved higher Kappa agreement with the lead coder than two trained human coders, highlighting their potential to reliably score open-response psychometric tests.

Automatic Scoring of an Open-Response Measure of Advanced Mind-Reading Using Large Language Models

Scientific research is inherently slow, complex, and highly specialized, relying on an iterative cycle of hypothesis generation and validation. Can we create an AI-powered biologist to automate these processes at scale? In this talk, I will outline the key ingredients for building such a system. I will first introduce Biomni, a generalist AI agent that integrates large language models with a vast ecosystem of specialized biological tools, software, and databases. By linking AI-driven reasoning with real-world execution, Biomni enables a broad range of biomedical applications. Building on this foundation, I will introduce BioDiscoveryAgent, an AI system for automated hypothesis generation that optimizes experimental design by identifying the most promising perturbations for screening. By leveraging literature-based reasoning, it significantly outperforms traditional Bayesian optimization approaches. Finally, beyond hypothesis generation, rigorous validation is essential. I will present POPPER, an AI-driven sequential falsification framework that automates hypothesis validation with statistical guarantees, ensuring scientific robustness at scale.

AI Agents for Accelerating Scientific Discovery: From Hypothesis Generation to Experimental Design

TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools

There exist approximately 166 billion small molecules, with 970 million deemed druglike. Similarly there is a vast pool of molecule candidates for new materials. This scarcity underscores the urgent need for innovative approaches, calling upon the NLP community to contribute significantly to medicine and material science. However, the challenges are manifold. Existing large language models (LLMs) alone are insufficient due to their tendency to generate erroneous claims confidently. Moreover, traditional knowledge bases do not adequately address the issue. This gap persists because chemistry language diverges significantly from natural language, demanding specialized domain knowledge, joint molecule and language modeling, and critical thinking. Using drug discovery, personalized drug synergy, and material discovery as case studies, I will present our approaches to tackle these challenges and turn an AI agent into a Medicinal Chemist or Material Scientist. I will share preliminary results from animal testing conducted on drug variants, and newly discovered material variants for efficient Organic Photovoltaic Devices proposed by AI algorithms.

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Assessing the Reliability and Validity of GPT-4 in Annotating Emotion Appraisal Ratings

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Fast Bilingual Grapheme-To-Phoneme Conversion

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Next from NAACL 2025

Assessing the Reliability and Validity of GPT-4 in Annotating Emotion Appraisal Ratings

Similar lecture

Fast Bilingual Grapheme-To-Phoneme Conversion

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads