Current research in natural language processing is highly dependent on carefully produced corpora. Most existing resources focus on English; some resources focus on languages such as Chinese and French; few resources deal with more than one language. This paper presents the Pirá dataset, a large set of questions and answers about the ocean and the Brazilian coast both in Portuguese and English. Pirá is, to the best of our knowledge, the first QA dataset with supporting texts in Portuguese, and, perhaps more importantly, the first bilingual QA dataset that includes this language. The Pirá dataset consists of 2261 properly curated question/answer (QA) sets in both languages. The QA sets were manually created based on two corpora: abstracts related to the Brazilian coast and excerpts of United Nation reports about the ocean. The QA sets were validated in a peer-review process with the dataset contributors. We discuss some of the advantages as well as limitations of Pirá, as this new resource can support a set of tasks in NLP such as question-answering, information retrieval, and machine translation.
CCS CONCEPTS• Applied computing → Document searching; Annotation.
The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for datato-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.
The Brazilian Exclusive Economic Zone, or the "Blue Amazon", with its extensive maritime area, is the primary means of transport for the country's foreign trade and is important due to its oil reserves, gas and other mineral resources, in addition to the significant influence on the Brazilian climate. We have manually built a question answering (QA) dataset based on crawled articles and have applied an off-the-shelf QA system based on a fine-tuned BERTimbau Model, achieving an F1-score of 47.0. More importantly, we explored how the proper visualization of attention weights can support helpful interpretations of the system's answers, which is critical in real environments.
Since the establishment of robotics in industrial applications, industrial robot programming involves therepetitive and time-consuming process of manually specifying a fixed trajectory, which results in machineidle time in terms of production and the necessity of completely reprogramming the robot for different tasks.The increasing number of robotics applications in unstructured environments requires not only intelligent butalso reactive controllers, due to the unpredictability of the environment and safety measures respectively. This paper presents a comparative analysis of two classes of Reinforcement Learning algorithms, value iteration (Q-Learning/DQN) and policy iteration (REINFORCE), applied to the discretized task of positioning a robotic manipulator in an obstacle-filled simulated environment, with no previous knowledge of the obstacles’ positions or of the robot arm dynamics. The agent’s performance and algorithm convergence are analyzed under different reward functions and on four increasingly complex test projects: 1-Degree of Freedom (DOF) robot, 2-DOF robot, Kuka KR16 Industrial robot, Kuka KR16 Industrial robot with random setpoint/obstacle placement. The DQN algorithm presented significantly better performance and reduced training time across all test projects and the third reward function generated better agents for both algorithms.
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