Existing approaches for disfluency detection typically require the existence of large annotated datasets. However, current datasets for this task are limited, suffer from class imbalance, and lack some types of disfluencies that are encountered in real-world scenarios. At the same time, augmentation techniques for disfluency detection are not able to model complex types of disfluencies. This limits such approaches to only performing pre-training since the generated data are not indicative of disfluencies that occur in real scenarios and, as a result, cannot be directly used for training disfluency detection models, as we experimentally demonstrate. This imposes significant constraints on the usefulness of such approaches in practice since real disfluencies still have to be collected in order to train the models. In this work, we propose Large-scale ARtificial Disfluency Generation (LARD), a method for automatically generating artificial disfluencies, and more specifically repairs, from fluent text. Unlike existing augmentation techniques, LARD can simulate all the different and complex types of disfluencies. In addition, it incorporates contextual embeddings into the disfluency generation to produce realistic, context-aware artificial disfluencies. LARD can be used effectively for training disfluency detection models, bypassing the requirement of annotated disfluent data. Our empirical evaluation shows that LARD outperforms existing rule-based augmentation methods and increases the accuracy of existing disfluency detectors. In addition, experiments demonstrate that the proposed method can be effectively used in a low-resource setup.

We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-scale, heterogeneous, graph-structured data are envisioned, harnessing recent evidence for relativistic quantum neural coding of (co-)behavioral states in the self-organizing brain, under competitive, multidimensional dynamics. The designs draw on a self-dual classical description – via special Clifford-Lipschitz operations – of spinorial quantum states within registers of at most 16 qubits for efficient encoding of exponentially large neural structures. Formally ‘trained’, Lorentz neural architectures with precisely one lateral layer of exclusively inhibitory interneurons accounting for anti-modalities, as well as their co-architectures with intra-layer connections are highlighted. The approach accommodates the fusion of up to 16 time-invariant interconnected (anti-)modalities and the crystallization of latent multidimensional patterns. Comprehensive insights are expected to be gained through applications to Multimodal Big Data, under diverse real-world scenarios.

Disfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropriate datasets. At the same time, existing datasets suffer from various issues, including class imbalance issues, which can significantly affect the performance of the model on rare classes, as it is demonstrated in this paper. To this end, we propose LARD, a method for generating complex and realistic artificial disfluencies with little effort. The proposed method can handle three of the most common types of disfluencies: repetitions, replacements, and restarts. In addition, we release a new large-scale dataset with disfluencies that can be used on four different tasks: disfluency detection, classification, extraction, and correction. Experimental results on the LARD dataset demonstrate that the data produced by the proposed method can be effectively used for detecting and removing disfluencies, while also addressing limitations of existing datasets.

The increase of social media use in recent years has shown potential also for the identification of specific trends in the data that could be used to locate earthquakes. In this work, we implemented a pipeline that uses Twitter data to identify locations of earthquakes and use the information to trigger EO data analysis. We tested the pipeline for almost a year over Japan, an area where earthquake events are frequent, as well as the use of social media in the population. Here, we show the results and discuss the potential development of such procedures. In the future, considering the rapid development and the increase of satellite constellations aimed at global coverage with short revisit times, algorithms of this kind could be used to prioritize satellite acquisitions for the detection of the areas most affected by earthquake damages.