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Found 13 papers, 3 papers with code
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.
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.
Ìtàkúròso: Exploiting Cross-Lingual Transferability for Natural Language 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.
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.
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.
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.
Spatiotemporal Spike-Pattern Selectivity in Single Mixed-Signal Neurons with Balanced Synapses
Realizing the potential of mixed-signal neuromorphic processors for ultra-low-power inference and learning requires efficient use of their inhomogeneous analog circuitry as well as sparse, time-based information encoding and processing.
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.
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.
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.
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.
Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks
However, wrong combination of hyper-parameters can produce poor quality vectors.
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.
