241 research outputs found
SAIN: Self-Attentive Integration Network for Recommendation
With the growing importance of personalized recommendation, numerous
recommendation models have been proposed recently. Among them, Matrix
Factorization (MF) based models are the most widely used in the recommendation
field due to their high performance. However, MF based models suffer from cold
start problems where user-item interactions are sparse. To deal with this
problem, content based recommendation models which use the auxiliary attributes
of users and items have been proposed. Since these models use auxiliary
attributes, they are effective in cold start settings. However, most of the
proposed models are either unable to capture complex feature interactions or
not properly designed to combine user-item feedback information with content
information. In this paper, we propose Self-Attentive Integration Network
(SAIN) which is a model that effectively combines user-item feedback
information and auxiliary information for recommendation task. In SAIN, a
self-attention mechanism is used in the feature-level interaction layer to
effectively consider interactions between multiple features, while the
information integration layer adaptively combines content and feedback
information. The experimental results on two public datasets show that our
model outperforms the state-of-the-art models by 2.13%Comment: SIGIR 201
Korean Vocational Secondary School Students’ Metacognition and Lifelong Learning
AbstractThe aim of this research is to analyze Korean Vocational Secondary School Students’ metacognition, attitude toward lifelong learning, and motivation factor to lifelong learning, and investigate whether these had an effect on their lifelong learning. This research analyzed after-school deeply as one of lifelong learning activities. I conduct frequency analysis, latent class analysis, and multiple regression analysis as methodology. The results were following: a) 75% of respondents have ever experienced the after-school learning; b) only 23% of the surveyed considered after-school learning is needed; c) the critical obstacles to after- school learning were deficiency of time(32.5%) and finance obstacle(28.2%); d) four underlying types of motivation to after-school learning were identified, namely, Class І(job Search), Class ІІ(leisure centered job skill), Class ІІІ(civic competency), and Class ІV(lack of motivation); e) Based on multiple regression analysis, as a predictor of effect on lifelong learning, variables including experience of after -school learning were significant
Look at the First Sentence: Position Bias in Question Answering
Many extractive question answering models are trained to predict start and
end positions of answers. The choice of predicting answers as positions is
mainly due to its simplicity and effectiveness. In this study, we hypothesize
that when the distribution of the answer positions is highly skewed in the
training set (e.g., answers lie only in the k-th sentence of each passage), QA
models predicting answers as positions can learn spurious positional cues and
fail to give answers in different positions. We first illustrate this position
bias in popular extractive QA models such as BiDAF and BERT and thoroughly
examine how position bias propagates through each layer of BERT. To safely
deliver position information without position bias, we train models with
various de-biasing methods including entropy regularization and bias
ensembling. Among them, we found that using the prior distribution of answer
positions as a bias model is very effective at reducing position bias,
recovering the performance of BERT from 37.48% to 81.64% when trained on a
biased SQuAD dataset.Comment: 13 pages, EMNLP 202
Robust Likelihood-Based Survival Modeling with Microarray Data
Gene expression data can be associated with various clinical outcomes. In particular, these data can be of importance in discovering survival-associated genes for medical applications. As alternatives to traditional statistical methods, sophisticated methods and software programs have been developed to overcome the high-dimensional difficulty of microarray data. Nevertheless, new algorithms and software programs are needed to include practical functions such as the discovery of multiple sets of survival-associated genes and the incorporation of risk factors, and to use in the R environment which many statisticians are familiar with. For survival modeling with microarray data, we have developed a software program (called rbsurv) which can be used conveniently and interactively in the R environment. This program selects survival-associated genes based on the partial likelihood of the Cox model and separates training and validation sets of samples for robustness. It can discover multiple sets of genes by iterative forward selection rather than one large set of genes. It can also allow adjustment for risk factors in microarray survival modeling. This software package, the rbsurv package, can be used to discover survival-associated genes with microarray data conveniently.
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations
Most weakly supervised named entity recognition (NER) models rely on
domain-specific dictionaries provided by experts. This approach is infeasible
in many domains where dictionaries do not exist. While a phrase retrieval model
was used to construct pseudo-dictionaries with entities retrieved from
Wikipedia automatically in a recent study, these dictionaries often have
limited coverage because the retriever is likely to retrieve popular entities
rather than rare ones. In this study, we present a novel framework, HighGEN,
that generates NER datasets with high-coverage pseudo-dictionaries.
Specifically, we create entity-rich dictionaries with a novel search method,
called phrase embedding search, which encourages the retriever to search a
space densely populated with various entities. In addition, we use a new
verification process based on the embedding distance between candidate entity
mentions and entity types to reduce the false-positive noise in weak labels
generated by high-coverage dictionaries. We demonstrate that HighGEN
outperforms the previous best model by an average F1 score of 4.7 across five
NER benchmark datasets.Comment: ACL 202
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