An Adaptive Trust Model Based on Fuzzy Logic

In cooperative environments is common that agents delegate tasks to each other to achieve their goals since an agent may not have the capabilities or resources to achieve its objectives alone. However, to select good partners, the agent needs to deal with information about the abilities, experience, and goals of their partners. In this situation, the lack or inaccuracy of information may affect the agent's judgment about a given partner; and hence, increases the risk to rely on an untrustworthy agent. Therefore, in this work, we present a trust model that combines different pieces of information, such as social image, reputation, and references to produce more precise information about the characteristics and abilities of agents. An important aspect of our trust model is that it can be easily configured to deal with different evaluation criteria. For instance, as presented in our experiments, the agents are able to select their partners by availability instead of the expertise level. Besides, the model allows the agents to decide when their own opinions about a partner are more relevant than the opinions received from third parties, and vice-versa. Such flexibility can be explored in dynamic scenarios, where the environment and the behavior of the agents might change constantly

Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models

Large multilingual models have inspired a new class of word alignment methods, which work well for the model's pretraining languages. However, the languages most in need of automatic alignment are low-resource and, thus, not typically included in the pretraining data. In this work, we ask: How do modern aligners perform on unseen languages, and are they better than traditional methods? We contribute gold-standard alignments for Bribri--Spanish, Guarani--Spanish, Quechua--Spanish, and Shipibo-Konibo--Spanish. With these, we evaluate state-of-the-art aligners with and without model adaptation to the target language. Finally, we also evaluate the resulting alignments extrinsically through two downstream tasks: named entity recognition and part-of-speech tagging. We find that although transformer-based methods generally outperform traditional models, the two classes of approach remain competitive with each other.Comment: EACL 202

AmericasNLI: Machine translation and natural language inference systems for Indigenous languages of the Americas

Little attention has been paid to the development of human language technology for truly low-resource languages—i.e., languages with limited amounts of digitally available text data, such as Indigenous languages. However, it has been shown that pretrained multilingual models are able to perform crosslingual transfer in a zero-shot setting even for low-resource languages which are unseen during pretraining. Yet, prior work evaluating performance on unseen languages has largely been limited to shallow token-level tasks. It remains unclear if zero-shot learning of deeper semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, a natural language inference dataset covering 10 Indigenous languages of the Americas. We conduct experiments with pretrained models, exploring zero-shot learning in combination with model adaptation. Furthermore, as AmericasNLI is a multiway parallel dataset, we use it to benchmark the performance of different machine translation models for those languages. Finally, using a standard transformer model, we explore translation-based approaches for natural language inference. We find that the zero-shot performance of pretrained models without adaptation is poor for all languages in AmericasNLI, but model adaptation via continued pretraining results in improvements. All machine translation models are rather weak, but, surprisingly, translation-based approaches to natural language inference outperform all other models on that task

AmericasNLI : machine translation and natural language inference systems for Indigenous languages of the Americas

Little attention has been paid to the development of human language technology for truly low-resource languages - i.e., languages with limited amounts of digitally available text data, such as Indigenous languages. However, it has been shown that pretrained multilingual models are able to perform crosslingual transfer in a zero-shot setting even for low-resource languages which are unseen during pretraining. Yet, prior work evaluating performance on unseen languages has largely been limited to shallow token-level tasks. It remains unclear if zero-shot learning of deeper semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, a natural language inference dataset covering 10 Indigenous languages of the Americas. We conduct experiments with pretrained models, exploring zero-shot learning in combination with model adaptation. Furthermore, as AmericasNLI is a multiway parallel dataset, we use it to benchmark the performance of different machine translation models for those languages. Finally, using a standard transformer model, we explore translation-based approaches for natural language inference. We find that the zero-shot performance of pretrained models without adaptation is poor for all languages in AmericasNLI, but model adaptation via continued pretraining results in improvements. All machine translation models are rather weak, but, surprisingly, translation-based approaches to natural language inference outperform all other models on that task.Facebook AI ResearchMicrosoft ResearchGoogle ResearchInstitute of Computational Linguistics at the University of ZurichNAACL Emerging Regions FundComunidad ElotlSnorkel A

A multi-agent systems\' model for knowledge sharing using communitary social networks.

Este trabalho apresenta um modelo para sistemas multiagentes constituídos por agentes de informação destinados a auxiliar comunidades humanas que partilham conhecimento. Tais agentes são cientes do entorno social dos usuários, pois possuem representações do conhecimento dos mesmos e também das redes sociais que os circundam, organizadas subjetivamente. Conceitos pertencentes às suas ontologias são estendidos com informação organizacional para representar de forma explícita as situações nas quais foram aprendidos e utilizados. Discute-se como tais agentes autônomos podem raciocinar sobre o uso e a privacidade de conceitos em termos de construções organizacionais, possibilitando raciocinar sobre papéis sociais em comunidades abertas na Internet.This work presents a model for multi-agent systems for information agents supporting information-sharing communities. Such agents are socially aware in the sense that they have representations of the users\' knowledge and also of their social networks, which are subjectively organized. Concepts in their ontologies are extended with organizational information to record explicitly the situations in which they were learned and used. It is discussed how such autonomous agents are allowed to reason about concept usage and privacy in terms of organizational constructs, paving the way to reason about social roles in open Internet communities

Integrating Knowledge Centered MAS through Organizational Links

This work presents a model in which concepts in ontologies are extended with organizational information to explicitly express the situation in which they were learned and used. It is discussed how autonomous agents are allowed to reason about concept usage and privacy in terms of organizational constructs, paving the way to reason about social roles in open Web communities. A peer-to-peer application following the model is described. We depart from a specific organization model, MOISE+, briefly presented here

An Adaptive Trust Model Based on Fuzzy Logic

In cooperative environments is common that agents delegate tasks to each other to achieve their goals since an agent may not have the capabilities or resources to achieve its objectives alone. However, to select good partners, the agent needs to deal with information about the abilities, experience, and goals of their partners. In this situation, the lack or inaccuracy of information may affect the agent's judgment about a given partner; and hence, increases the risk to rely on an untrustworthy agent. Therefore, in this work, we present a trust model that combines different pieces of information, such as social image, reputation, and references to produce more precise information about the characteristics and abilities of agents. An important aspect of our trust model is that it can be easily configured to deal with different evaluation criteria. For instance, as presented in our experiments, the agents are able to select their partners by availability instead of the expertise level. Besides, the model allows the agents to decide when their own opinions about a partner are more relevant than the opinions received from third parties, and vice-versa. Such flexibility can be explored in dynamic scenarios, where the environment and the behavior of the agents might change constantly

Overview de GUA-SPA en IberLEF 2023: Análisis de Cambio de Código en Guaraní-Español

We present the first shared task for detecting and analyzing codeswitching in Guarani and Spanish, GUA-SPA at IberLEF 2023. The challenge consisted of three tasks: identifying the language of a token, NER, and a novel task of classifying the way a Spanish span is used in the code-switched context. We annotated a corpus of 1500 texts extracted from news articles and tweets, around 25 thousand tokens, with the information for the tasks. Three teams took part in the evaluation phase, obtaining in general good results for Task 1, and more mixed results for Tasks 2 and 3.Presentamos la primera competencia de detección y análisis de cambio de código en guaraní y español, GUA-SPA en IberLEF 2023. La competencia constó de tres tareas: identificar el idioma de cada token, NER, y una nueva tarea de clasificar la forma en que se usan los segmentos en español en el contexto del cambio de código. Anotamos un corpus de 1500 textos extraídos de artículos de prensa y tweets, alrededor de 25 mil tokens, con la información necesaria para las tareas. Tres equipos participaron de la fase de evaluación, obteniendo en general buenos resultados para la tarea 1, y resultados más dispares para las tareas 2 y 3