1,257 research outputs found
Learning to select data for transfer learning with Bayesian Optimization
Domain similarity measures can be used to gauge adaptability and select
suitable data for transfer learning, but existing approaches define ad hoc
measures that are deemed suitable for respective tasks. Inspired by work on
curriculum learning, we propose to \emph{learn} data selection measures using
Bayesian Optimization and evaluate them across models, domains and tasks. Our
learned measures outperform existing domain similarity measures significantly
on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We
show the importance of complementing similarity with diversity, and that
learned measures are -- to some degree -- transferable across models, domains,
and even tasks.Comment: EMNLP 2017. Code available at:
https://github.com/sebastianruder/learn-to-select-dat
An improved neural network model for joint POS tagging and dependency parsing
We propose a novel neural network model for joint part-of-speech (POS)
tagging and dependency parsing. Our model extends the well-known BIST
graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating
a BiLSTM-based tagging component to produce automatically predicted POS tags
for the parser. On the benchmark English Penn treebank, our model obtains
strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+%
absolute improvements to the BIST graph-based parser, and also obtaining a
state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental
results on parsing 61 "big" Universal Dependencies treebanks from raw texts
show that our model outperforms the baseline UDPipe (Straka and Strakov\'a,
2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS
score. In addition, with our model, we also obtain state-of-the-art downstream
task scores for biomedical event extraction and opinion analysis applications.
Our code is available together with all pre-trained models at:
https://github.com/datquocnguyen/jPTDPComment: 11 pages; In Proceedings of the CoNLL 2018 Shared Task: Multilingual
Parsing from Raw Text to Universal Dependencies, to appea
DeSyRe: on-Demand System Reliability
The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints
Design of a Scan Chain for Side Channel Attacks on AES Cryptosystem for Improved Security
Scan chain-based attacks are side-channel attacks focusing on one of the most significant features of hardware test circuitry. A technique called Design for Testability (DfT) involves integrating certain testability components into a hardware design. However, this creates a side channel for cryptanalysis, providing crypto devices vulnerable to scan-based attacks. Advanced Encryption Standard (AES) has been proven as the most powerful and secure symmetric encryption algorithm announced by USA Government and it outperforms all other existing cryptographic algorithms. Furthermore, the on-chip implementation of private key algorithms like AES has faced scan-based side-channel attacks. With the aim of protecting the data for secure communication, a new hybrid pipelined AES algorithm with enhanced security features is implemented. This paper proposes testing an AES core with unpredictable response compaction and bit level-masking throughout the scan chain process. A bit-level scan flipflop focused on masking as a scan protection solution for secure testing. The experimental results show that the best security is provided by the randomized addition of masked scan flipflop through the scan chain and also provides minimal design difficulty and power expansion overhead with some negligible delay measures. Thus, the proposed technique outperforms the state-of-the-art LUT-based S-box and the composite sub-byte transformation model regarding throughput rate 2 times and 15 times respectively. And security measured in the avalanche effect for the sub-pipelined model has been increased up to 95 per cent with reduced computational complexity. Also, the proposed sub-pipelined S-box utilizing a composite field arithmetic scheme achieves 7 per cent area effectiveness and 2.5 times the hardware complexity compared to the LUT-based model
AN EVALUATION OF ALTERNATİVE METHODS FOR FINANCIAL PERFORMANCE: EVIDENCE FROM TURKEY (ISTANBUL STOCK EXCHANGE)
This study aims to determine which financial performance ranking methods accurately predict the actual rankings by using multiple criteria decision techniques, and it compares the accuracy of the rankings based on the financial performance indicators and the market based approach which involves market value and average return. Companies listed in BIST50 index (Borsa Istanbul) were investigated, as a result, when considering average return, Promethee and Copras produced similar and consistent rankings. Besides, since it places emphasize on the functional structures of variables, Promethee method was noted to produce the most accurate rankings, thus deemed most effective method helping investors give rational decisions
Viability of Sequence Labeling Encodings for Dependency Parsing
Programa Oficial de Doutoramento en Computación . 5009V01[Abstract]
This thesis presents new methods for recasting dependency parsing as
a sequence labeling task yielding a viable alternative to the traditional
transition- and graph-based approaches. It is shown that sequence labeling
parsers provide several advantages for dependency parsing, such
as: (i) a good trade-off between accuracy and parsing speed, (ii) genericity
which enables running a parser in generic sequence labeling software
and (iii) pluggability which allows using full parse trees as features to
downstream tasks.
The backbone of dependency parsing as sequence labeling are the encodings
which serve as linearization methods for mapping dependency
trees into discrete labels, such that each token in a sentence is associated
with a label. We introduce three encoding families comprising: (i)
head selection, (ii) bracketing-based and (iii) transition-based encodings
which are differentiated by the way they represent a dependency
tree as a sequence of labels. We empirically examine the viability of
the encodings and provide an analysis of their facets.
Furthermore, we explore the feasibility of leveraging external complementary
data in order to enhance parsing performance. Our sequence
labeling parser is endowed with two kinds of representations. First,
we exploit the complementary nature of dependency and constituency
parsing paradigms and enrich the parser with representations from both
syntactic abstractions. Secondly, we use human language processing
data to guide our parser with representations from eye movements.
Overall, the results show that recasting dependency parsing as sequence
labeling is a viable approach that is fast and accurate and provides
a practical alternative for integrating syntax in NLP tasks.[Resumen]
Esta tesis presenta nuevos métodos para reformular el análisis sintáctico
de dependencias como una tarea de etiquetado secuencial, lo
que supone una alternativa viable a los enfoques tradicionales basados
en transiciones y grafos. Se demuestra que los analizadores de etiquetado
secuencial ofrecen varias ventajas para el análisis sintáctico de
dependencias, como por ejemplo (i) un buen equilibrio entre la precisión
y la velocidad de análisis, (ii) la genericidad que permite ejecutar
un analizador en un software genérico de etiquetado secuencial y (iii)
la conectividad que permite utilizar el árbol de análisis completo como
características para las tareas posteriores.
El pilar del análisis sintáctico de dependencias como etiquetado secuencial
son las codificaciones que sirven como métodos de linealización
para transformar los árboles de dependencias en etiquetas discretas, de
forma que cada token de una frase se asocia con una etiqueta. Introducimos
tres familias de codificación que comprenden: (i) selección de
núcleos, (ii) codificaciones basadas en corchetes y (iii) codificaciones basadas
en transiciones que se diferencian por la forma en que representan
un árbol de dependencias como una secuencia de etiquetas. Examinamos
empíricamente la viabilidad de las codificaciones y ofrecemos un
análisis de sus facetas.
Además, exploramos la viabilidad de aprovechar datos complementarios
externos para mejorar el rendimiento del análisis sintáctico. Dotamos
a nuestro analizador sintáctico de dos tipos de representaciones. En
primer lugar, explotamos la naturaleza complementaria de los paradigmas
de análisis sintáctico de dependencias y constituyentes, enriqueciendo
el analizador sintáctico con representaciones de ambas abstracciones
sintácticas. En segundo lugar, utilizamos datos de procesamiento del
lenguaje humano para guiar nuestro analizador con representaciones de
los movimientos oculares.
En general, los resultados muestran que la reformulación del análisis
sintáctico de dependencias como etiquetado de secuencias es un enfoque
viable, rápido y preciso, y ofrece una alternativa práctica para integrar
la sintaxis en las tareas de PLN.[Resumo]
Esta tese presenta novos métodos para reformular a análise sintáctica
de dependencias como unha tarefa de etiquetaxe secuencial, o que
supón unha alternativa viable aos enfoques tradicionais baseados en
transicións e grafos. Demóstrase que os analizadores de etiquetaxe secuencial
ofrecen varias vantaxes para a análise sintáctica de dependencias,
por exemplo (i) un bo equilibrio entre a precisión e a velocidade
de análise, (ii) a xenericidade que permite executar un analizador nun
software xenérico de etiquetaxe secuencial e (iii) a conectividade que
permite empregar a árbore de análise completa como características
para as tarefas posteriores.
O piar da análise sintáctica de dependencias como etiquetaxe secuencial
son as codificacións que serven como métodos de linealización para
transformar as árbores de dependencias en etiquetas discretas, de forma
que cada token dunha frase se asocia cunha etiqueta. Introducimos
tres familias de codificación que comprenden: (i) selección de núcleos,
(ii) codificacións baseadas en corchetes e (iii) codificacións baseadas en
transicións que se diferencian pola forma en que representan unha árbore
de dependencia como unha secuencia de etiquetas. Examinamos
empíricamente a viabilidade das codificacións e ofrecemos unha análise
das súas facetas.
Ademais, exploramos a viabilidade de aproveitar datos complementarios
externos para mellorar o rendemento da análise sintáctica. O noso
analizador sintáctico de etiquetaxe secuencial está dotado de dous tipos
de representacións. En primeiro lugar, explotamos a natureza complementaria
dos paradigmas de análise sintáctica de dependencias e constituíntes
e enriquecemos o analizador sintáctico con representacións de
ambas abstraccións sintácticas. En segundo lugar, empregamos datos
de procesamento da linguaxe humana para guiar o noso analizador con
representacións dos movementos oculares.
En xeral, os resultados mostran que a reformulación da análise sintáctico
de dependencias como etiquetaxe de secuencias é un enfoque
viable, rápido e preciso, e ofrece unha alternativa práctica para integrar
a sintaxe nas tarefas de PLN.This work has been carried out thanks to the funding from
the European Research Council (ERC), under the European Union’s
Horizon 2020 research and innovation programme (FASTPARSE, grant
agreement No 714150)
- …