Conversational AI (ConvAI) systems are gaining growing importance as an alternative for more natural interaction with digital services. In this context, Large Language Models (LLMs) have opened new possibilities for less restricted interaction and richer natural language understanding. However, despite their advanced capabilities, LLMs can pose accuracy and reliability problems, as they sometimes generate factually incorrect or contextually inappropriate content that does not fulfill the regulations or business rules of a specific application domain. In addition, they still do not possess the capability to adjust to users’ needs and preferences, showing emotional awareness, while concurrently adhering to the regulations and limitations of their designated domain. In this paper we present the TrustBoost project, which addresses the challenge of improving trustworthiness of ConvAI from two dimensions: cognition (adaptability, flexibility, compliance, and performance) and affectivity (familiarity, emotional dimension, and perception). The duration of the project is from September 2024 to December 2027.

In this work, we make the case of quality over quantity when training a MT system for a medium-to-low-resource language pair, namely Catalan-English. We compile our training corpus out of existing resources of varying quality and a new high-quality corpus. We also provide new evaluation translation datasets in three different domains. In the process of building Catalan-English parallel resources, we evaluate the impact of drastically filtering alignments in the resulting MT engines. Our results show that even when resources are limited, as in this case, it is worth filtering for quality. We further explore the cross-lingual transfer learning capabilities of the proposed model for parallel corpus filtering by applying it to other languages. All resources generated in this work are released under open license to encourage the development of language technology in Catalan.

This paper describes the participation of the BSC team in the WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared Task. The system aims to solve the Subtask 2: Wikipedia cultural heritage articles, which involves translation in four Romance languages: Catalan, Italian, Occitan and Romanian. The submitted system is a multilingual semi-supervised machine translation model. It is based on a pre-trained language model, namely XLM-RoBERTa, that is later fine-tuned with parallel data obtained mostly from OPUS. Unlike other works, we only use XLM to initialize the encoder and randomly initialize a shallow decoder. The reported results are robust and perform well for all tested languages.