Explicit Interaction Model towards Text Classification

Text classification is one of the fundamental tasks in natural language processing. Recently, deep neural networks have achieved promising performance in the text classification task compared to shallow models. Despite of the significance of deep models, they ignore the fine-grained (matching signals between words and classes) classification clues since their classifications mainly rely on the text-level representations. To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task. In particular, we design a novel framework, EXplicit interAction Model (dubbed as EXAM), equipped with the interaction mechanism. We justified the proposed approach on several benchmark datasets including both multi-label and multi-class text classification tasks. Extensive experimental results demonstrate the superiority of the proposed method. As a byproduct, we have released the codes and parameter settings to facilitate other researches.Comment: 8 page

Labeled Memory Networks for Online Model Adaptation

Augmenting a neural network with memory that can grow without growing the number of trained parameters is a recent powerful concept with many exciting applications. We propose a design of memory augmented neural networks (MANNs) called Labeled Memory Networks (LMNs) suited for tasks requiring online adaptation in classification models. LMNs organize the memory with classes as the primary key.The memory acts as a second boosted stage following a regular neural network thereby allowing the memory and the primary network to play complementary roles. Unlike existing MANNs that write to memory for every instance and use LRU based memory replacement, LMNs write only for instances with non-zero loss and use label-based memory replacement. We demonstrate significant accuracy gains on various tasks including word-modelling and few-shot learning. In this paper, we establish their potential in online adapting a batch trained neural network to domain-relevant labeled data at deployment time. We show that LMNs are better than other MANNs designed for meta-learning. We also found them to be more accurate and faster than state-of-the-art methods of retuning model parameters for adapting to domain-specific labeled data.Comment: Accepted at AAAI 2018, 8 page

Comparación entre la gravedad del paciente y la carga de trabajo de la enfermería antes y después de la ocurrencia de eventos adversos en ancianos con cuidados críticos

Indexación: Scopus.Objective: to compare the patient severity and the nursing workload before and after the occurrence of moderate and severe adverse events in elderly hospitalized at intensive care units. Method: comparative study developed at nine intensive therapy units of a University Hospital in São Paulo. The events were collected from the patient histories and classified as moderate and severe according to the World Health Organization. For the severity analysis, the Simplified Acute Physiologic Score II was used and, for the workload analysis, the Nursing Activities Score was applied 24 hours before and after the moderate and severe event. The t-test with 5% significance was used to compare the mean clinical severity and workload scores before and after the event. Results: the sample consisted of 315 elderly, 94 (29.8%) of whom were victims of moderate and severe events at the units. Among the 94 events, the clinical process and procedure type was predominant (40.0%). The installation and maintenance of therapeutic artifacts and catheters were the prevalent interventions that resulted in moderate (76.5%) physiopathological damage (66.0%). The mean workload score (75.19%) dropped 24 hours after the occurrence of the event (71.97%, p=0.008), and the severity, represented by the probability of death, increased from 22.0% to 29.0% after the event (p=0.045). Conclusion: in the patient safety context, the identification of the changes in clinical conditions and the nursing workload in elderly victims of events supports the prevention of these occurrences. © 2018, Universidade Federal de Santa Catarina. All rights reserved.Objetivo: comparar a gravidade do paciente e a carga de trabalho de enfermagem antes e após a ocorrência de evento adverso moderado e grave em idosos internados em unidades de terapia intensiva. Método: estudo comparativo, realizado em nove unidades de terapia intensiva de um Hospital Universitário de São Paulo. Os eventos foram coletados dos prontuários dos pacientes e classificados em moderados e graves segundo a Organização Mundial de Saúde. A análise da gravidade foi realizada segundo o Symplified Acute Phsiologic Score II e a carga de trabalho segundo o Nursing Activities Score, 24 horas antes e depois do evento moderado e grave. O teste t, com significância de 5%, foi utilizado para a comparação das médias da gravidade clínica e da carga de trabalho, antes e após o evento. Resultados: a amostra foi composta por 315 idosos, sendo que 94 (29,8%) sofreram eventos moderados e graves nas unidades. Dos 94 eventos, predominou o tipo processo clínico e procedimento (40,0%). A instalação e manutenção de artefatos terapêuticos e cateteres foram as intervenções prevalentes que resultaram em danos fisiopatológicos (66,0%), de grau moderado (76,5%). A média de pontuação da carga de trabalho (75,19%) diminuiu 24 horas após a ocorrência do evento (71,97%, p=0,008) e, a gravidade, representada pela probabilidade de morte, aumentou de 22,0% para 29,0% depois do evento (p=0,045). Conclusão: no contexto da segurança do paciente, a identificação das alterações nas condições clínicas e na carga de trabalho de enfermagem em idosos que sofrem eventos subsidiam a prevenção dessas ocorrências.Objetivo: comparar la gravedad del paciente y la carga de trabajo en enfermería antes y después de ocurrir un evento adverso moderado y grave en ancianos internados en unidades de terapia intensiva. Método: estudio comparativo realizado en nueve unidades de terapia intensiva de un Hospital Universitario de São Paulo. Los eventos fueron obtenidos a través de los prontuarios de los pacientes y clasificados en moderados y graves según la Organización Mundial de la Salud. El análisis sobre la gravedad fue realizado de acuerdo al Symplified Acute Physiologic Score II y la carga de trabajo se hizo conforme al Nursing Activities Score, 24 horas antes y después del evento moderado y grave. El test t, con una significancia del 5%, fue utilizado para la comparación de los promedios de la gravedad clínica y de la carga de trabajo antes y después del evento. Resultados: la muestra incluyó 315 ancianos, siendo que 94 (29,8%) sufrieron eventos moderados y graves en las unidades. De los 94 eventos, predominó el tipo de proceso clínico y el procedimiento (40,0%). La instalación y mantenimiento de artefactos terapéuticos y catéteres fueron las intervenciones prevalentes que resultaron en daños fisiopatológicos (66,0%) y de grado moderado (76,5%). El promedio de puntuación de la carga de trabajo (75,19%) disminuyó 24 horas después de ocurrido el evento (71,97%, p=0,008) y la gravedad, representada por la probabilidad de muerte, aumentó de 22,0% para 29,0% después del evento (p=0,045). Conclusion: en el contexto de seguridad del paciente, la identificación de las alteraciones en las condiciones clínicas y en la carga de trabajo de enfermería en los ancianos que sufren eventos subsidia la prevención de tales ocurrencias.http://ref.scielo.org/wcg6x

Speech vocoding for laboratory phonology

Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal. Our goal is to make a step towards bridging phonology and speech processing and to contribute to the program of Laboratory Phonology. We show three application examples for laboratory phonology: compositional phonological speech modelling, a comparison of phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The featural representations of the following three phonological systems are considered in this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English (SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded speech, we conclude that the latter achieves slightly better results than the former. However, GP - the most compact phonological speech representation - performs comparably to the systems with a higher number of phonological features. The parametric TTS based on phonological speech representation, and trained from an unlabelled audiobook in an unsupervised manner, achieves intelligibility of 85% of the state-of-the-art parametric speech synthesis. We envision that the presented approach paves the way for researchers in both fields to form meaningful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. On the one hand, laboratory phonologists might test the applied concepts of their theoretical models, and on the other hand, the speech processing community may utilize the concepts developed for the theoretical phonological models for improvements of the current state-of-the-art applications