AIMC Topic: Language

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Gender Stereotypes in Natural Language: Word Embeddings Show Robust Consistency Across Child and Adult Language Corpora of More Than 65 Million Words.

Psychological science
Stereotypes are associations between social groups and semantic attributes that are widely shared within societies. The spoken and written language of a society affords a unique way to measure the magnitude and prevalence of these widely shared colle...

Multitask Learning and Reinforcement Learning for Personalized Dialog Generation: An Empirical Study.

IEEE transactions on neural networks and learning systems
Open-domain dialog generation, which is a crucial component of artificial intelligence, is an essential and challenging problem. In this article, we present a personalized dialog system, which leverages the advantages of multitask learning and reinfo...

Directions in abusive language training data, a systematic review: Garbage in, garbage out.

PloS one
Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effecti...

Stacked DeBERT: All attention in incomplete data for text classification.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose Stacked DeBERT, short for StackedDenoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel encoding sc...

Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning.

Scientific reports
In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art ...

Analysis of readability and structural accuracy in SNOMED CT.

BMC medical informatics and decision making
BACKGROUND: The increasing adoption of ontologies in biomedical research and the growing number of ontologies available have made it necessary to assure the quality of these resources. Most of the well-established ontologies, such as the Gene Ontolog...

Semi-supervised disentangled framework for transferable named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale la...

Transforming task representations to perform novel tasks.

Proceedings of the National Academy of Sciences of the United States of America
An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superh...

Predicting risk of dyslexia with an online gamified test.

PloS one
Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified ...

Mixed-Level Neural Machine Translation.

Computational intelligence and neuroscience
Building the first Russian-Vietnamese neural machine translation system, we faced the problem of choosing a translation unit system on which source and target embeddings are based. Available homogeneous translation unit systems with the same translat...