Search Results for author:
Found 16 papers, 5 papers with code
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work.
Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks
However, wrong combination of hyper-parameters can produce poor quality vectors.
The Challenge of Diacritics in Yoruba Embeddings
The major contributions of this work include the empirical establishment of a better performance for Yoruba embeddings from undiacritized (normalized) dataset and provision of new analogy sets for evaluation.
Bipol: Multi-axes Evaluation of Bias with Explainability in Benchmark Datasets
Hence, we also contribute a new, large Swedish bias-labelled dataset (of 2 million samples), translated from the English version and train the SotA mT5 model on it.
Synaptic Integration of Spatiotemporal Features with a Dynamic Neuromorphic Processor
Here, we investigate synaptic integration of spatiotemporal spike patterns with multiple dynamic synapses on point-neurons in the DYNAP-SE neuromorphic processor, which offers a complementary resource-efficient, albeit less flexible, approach to feature detection.
Exploring Swedish & English fastText Embeddings for NER with the Transformer
To achieve a good network performance in natural language processing (NLP) downstream tasks, several factors play important roles: dataset size, the right hyper-parameters, and well-trained embeddings.
Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora
In this work, we show that the difference in performance of embeddings from differently sourced data for a given language can be due to other factors besides data size.
Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses
Mixed-signal neuromorphic processors with brain-like organization and device physics offer an ultra-low-power alternative to the unsustainable developments of conventional deep learning and computing.
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning
Perplexity score (an automated intrinsic language model metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models, with results that indicate that the capacity for transfer learning can be exploited with considerable success.
HaT5: Hate Language Identification using Text-to-Text Transfer Transformer
We investigate the performance of a state-of-the art (SoTA) architecture T5 (available on the SuperGLUE) and compare with it 3 other previous SoTA architectures across 5 different tasks from 2 relatively diverse datasets.
ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language
This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection.
AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages
The language with the most transferable properties is the Nigerian Pidgin English, with a human-likeness score of 78. 1%, of which 34. 4% are unanimous.
State-of-the-art in Open-domain Conversational AI: A Survey
Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI.
Vector Representations of Idioms in Conversational Systems
We experiment with three instances of the SoTA dialogue model, Dialogue Generative Pre-trained Transformer (DialoGPT), for conversation generation.
T5 for Hate Speech, Augmented Data and Ensemble
Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods like data augmentation and ensemble may have on the best model, if any.
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons
With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive speech recognition.
