Building a Corpus for Personality-dependent Natural Language Understanding and Generation (L18-1)
Copied to clipboard
| Challenge: | The computational treatment of human personality is central to the development of NLP applications. |
| Approach: | They propose to use the b5 corpus to generate controlled and free (non-topic specific) texts . preliminary results of personality recognition from text are presented . |
| Outcome: | The proposed corpus is the largest resource of this kind to be made available for research purposes in the Brazilian Portuguese language. |
Similar Papers
Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language (P19-2)
Copied to clipboard
| Challenge: | Natural Language Generation (NLG) is a promising area in Natural Language Processing (NLP) . |
| Approach: | They present a review of the literature on Natural Language Generation in Brazilian Portuguese. |
| Outcome: | The proposed approaches are based on the Abstract Meaning Representation formalism and have potential future directions. |
Manipulating the Perceived Personality Traits of Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Psychology research has long explored aspects of human personality like extroversion, agreeableness and emotional stability, three of the personality traits that make up the ‘Big Five’. |
| Approach: | They propose to use text generated from large language models to evaluate perceived personality traits and to frame them as tools for controlling personas in dialog systems. |
| Outcome: | The proposed models predict personality traits in different contexts and can be manipulated in a predictable way. |
Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations (D19-63)
Copied to clipboard
| Challenge: | Abstract Meaning Representation and Brazilian Portuguese (BP) are selected as semantic representation and language, respectively. |
| Approach: | They propose to use Brazilian Portuguese and Abstract Meaning Representation as semantic representations for NLG. |
| Outcome: | The proposed methods were evaluated on two datasets (one automatically generated and another human-generated) to compare the performance in a real context. |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
A Survey of Automatic Personality Detection from Texts (2020.coling-main)
Copied to clipboard
| Challenge: | Personality profiling has long been used in psychology to predict life outcomes. |
| Approach: | They present the trajectory of automatic personality detection from purely psychology approaches to the latest purely natural language processing approaches on large social media datasets. |
| Outcome: | The proposed models have been compared with the most recent approaches on large social media datasets. |
Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
Copied to clipboard
| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Modeling, Evaluating, and Embodying Personality in LLMs: A Survey (2025.findings-emnlp)
Copied to clipboard
Iago Alves Brito, Julia Soares Dollis, Fernanda Bufon Färber, Pedro Schindler Freire Brasil Ribeiro, Rafael Teixeira Sousa, Arlindo Rodrigues Galvão Filho
| Challenge: | This survey provides a comprehensive overview of the LLM-driven personality scenario. |
| Approach: | This survey provides a comprehensive overview of the LLM-driven personality scenario. |
| Outcome: | The proposed taxonomy analyzes the limitations of existing methods and identifies key research gaps. |
On Text-based Personality Computing: Challenges and Future Directions (2023.findings-acl)
Copied to clipboard
Qixiang Fang, Anastasia Giachanou, Ayoub Bagheri, Laura Boeschoten, Erik-Jan van Kesteren, Mahdi Shafiee Kamalabad, Daniel Oberski
| Challenge: | Text-based personality computing (TPC) is a popular alternative to self-report questionnaires. |
| Approach: | They propose 15 challenges that are relevant to NLP research . they propose to combine perspectives from both NLP and social sciences . |
| Outcome: | The proposed approach is based on text-based personality computing (TPC) the proposed approach can be used to improve the quality of personality-based research. |
The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
| Outcome: | The proposed corpus is searchable through a couple of well-established corpus infrastructures. |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
Copied to clipboard
| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |