The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for data-to-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representations. This study compares the most often employed methods for generating Brazilian Portuguese texts from structured data. Results suggest that explicit intermediate steps in the generation process produce better texts than the ones generated by neural end-to-end architectures, avoiding data hallucination while better generalizing to unseen inputs. Code and corpus are publicly available.

In the initial stage of human life, communication, seen as a process of social interaction, was always the best way to reach consensus between the parties. Understanding and credibility in this process are essential for the mutual agreement to be validated. But, how to do it so that this communication reaches the great mass? This is the main challenge when what is sought is the dissemination of information and its approval. In this context, this study presents the ALT software, developed from original readability metrics adapted to the Portuguese language, available on the web, to reduce communication difficulties. The development of the software was motivated by the theory of communicative action of Habermas, which uses a multidisciplinary style to measure the credibility of the discourse in the communication channels used to build and maintain a safe and healthy relationship with the public.

Today's world is full of sensors, and the higher your nation-state is on the advanced-industrial food chain, the more likely it is that you carry a sensor on your person every minute of every day (and for many, even while asleep). That matters: the data collected by these sensors can be stored, analyzed, and weaponized. And although most of today's data collectors are for-profit corporations, there are dire risks alongside the potential for breakthroughs in areas such as medicine and global warming. The collection, analysis, storage, and theft of information about you have lethal implications; both for you as an individual and for all of us in terms of interstate war. In his 2018 book AI Superpowers, author and entrepreneur Kai-Fu Lee likened big data to the new crude oil and noted that insofar as the analogy holds, that would make the People's Republic of China (PRC) the world's data Saudi Arabia.

Phaidra is building the future of industrial automation. The world today is filled with static, monolithic infrastructure. Factories, power plants, buildings, etc. operate the same they've operated for decades -- because the controls programming is hard-coded. Thousands of lines of rules and heuristics that define how the machines interact with each other. The result of all this hard-coding is that facilities are frozen in time, unable to adapt to their environment while their performance slowly degrades.

"Barcelona has triumphed over Madrid as Spain's startup capital because of the deal flow and the talent," explains Miquel Martí, Tech Barcelona's CEO. "International talent is attracted by the lifestyle, the strength of the ecosystem, and the presence of international companies." According to Barcelona & Catalonia Startup Hub--the region's startup directory--there are over 1,900 startups in Catalonia, mostly concentrated in Barcelona; since 2016, the number has grown by over 75 percent. The city has long had a strong tradition of health-tech startups fueled by university and regional government collaboration, but successful founders have been funding, supporting, and starting other companies, as well as diversifying into construction, mobility, and sustainability. There's also been a slow trickle of fintech companies from London, lured by post-Brexit border-free banking, good weather, a lower cost of living--and the beach.

Transformers allow attention between all pairs of tokens, but there is reason to believe that most of these connections - and their quadratic time and memory - may not be necessary. But which ones? We evaluate the impact of sparsification patterns with a series of ablation experiments. First, we compare masks based on syntax, lexical similarity, and token position to random connections, and measure which patterns reduce performance the least. We find that on three common finetuning tasks even using attention that is at least 78% sparse can have little effect on performance if applied at later transformer layers, but that applying sparsity throughout the network reduces performance significantly. Second, we vary the degree of sparsity for three patterns supported by previous work, and find that connections to neighbouring tokens are the most significant. Finally, we treat sparsity as an optimizable parameter, and present an algorithm to learn degrees of neighboring connections that gives a fine-grained control over the accuracy-sparsity trade-off while approaching the performance of existing methods.

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning.

The rapid development of single-modal pre-training has prompted researchers to pay more attention to cross-modal pre-training methods. In this paper, we propose a unified-modal speech-unit-text pre-training model, SpeechUT, to connect the representations of a speech encoder and a text decoder with a shared unit encoder. Leveraging hidden-unit as an interface to align speech and text, we can decompose the speech-to-text model into a speech-to-unit model and a unit-to-text model, which can be jointly pre-trained with unpaired speech and text data respectively. Our proposed SpeechUT is fine-tuned and evaluated on automatic speech recognition (ASR) and speech translation (ST) tasks. Experimental results show that SpeechUT gets substantial improvements over strong baselines, and achieves state-of-the-art performance on both the LibriSpeech ASR and MuST-C ST tasks. To better understand the proposed SpeechUT, detailed analyses are conducted. The code and pre-trained models are available at https://aka.ms/SpeechUT.

Automated Human Activity Recognition has long been a problem of great interest in human-centered and ubiquitous computing. In the last years, a plethora of supervised learning algorithms based on deep neural networks has been suggested to address this problem using various modalities. While every modality has its own limitations, there is one common challenge. Namely, supervised learning requires vast amounts of annotated data which is practically hard to collect. In this paper, we benefit from the self-supervised learning paradigm (SSL) that is typically used to learn deep feature representations from unlabeled data. Moreover, we upgrade a contrastive SSL framework, namely SimCLR, widely used in various applications by introducing a temporal feature alignment procedure for Human Activity Recognition. Specifically, we propose integrating a dynamic time warping (DTW) algorithm in a latent space to force features to be aligned in a temporal dimension. Extensive experiments have been conducted for the unimodal scenario with inertial modality as well as in multimodal settings using inertial and skeleton data. According to the obtained results, the proposed approach has a great potential in learning robust feature representations compared to the recent SSL baselines, and clearly outperforms supervised models in semi-supervised learning. The code for the unimodal case is available via the following link: https://github.com/bulatkh/csshar_tfa.

This paper investigates an unsupervised approach towards deriving a universal, cross-lingual word embedding space, where words with similar semantics from different languages are close to one another. Previous adversarial approaches have shown promising results in inducing cross-lingual word embedding without parallel data. However, the training stage shows instability for distant language pairs. Instead of mapping the source language space directly to the target language space, we propose to make use of a sequence of intermediate spaces for smooth bridging. Each intermediate space may be conceived as a pseudo-language space and is introduced via simple linear interpolation. This approach is modeled after domain flow in computer vision, but with a modified objective function. Experiments on intrinsic Bilingual Dictionary Induction tasks show that the proposed approach can improve the robustness of adversarial models with comparable and even better precision. Further experiments on the downstream task of Cross-Lingual Natural Language Inference show that the proposed model achieves significant performance improvement for distant language pairs in downstream tasks compared to state-of-the-art adversarial and non-adversarial models.