Search Results for author: Tosin Adewumi

Found 8 papers, 1 papers with code

Vector Representations of Idioms in Conversational Systems

no code implementations7 May 2022 Tosin Adewumi, Foteini Liwicki, Marcus Liwicki

We experiment with three instances of the SoTA dialogue model, Dialogue Generative Pre-trained Transformer (DialoGPT), for conversation generation.

Information Retrieval Machine Translation

State-of-the-art in Open-domain Conversational AI: A Survey

no code implementations2 May 2022 Tosin Adewumi, Foteini Liwicki, Marcus Liwicki

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.

ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language

no code implementations15 Apr 2022 Tosin Adewumi, Lama Alkhaled, Hamam Mokayed, Foteini Liwicki, Marcus Liwicki

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

no code implementations11 Feb 2022 Sana Sabah Sabry, Tosin Adewumi, Nosheen Abid, György Kovacs, Foteini Liwicki, Marcus Liwicki

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.

Data Augmentation Explainable artificial intelligence +1

Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning

no code implementations12 Oct 2021 Tosin Adewumi, Rickard Brännvall, Nosheen Abid, Maryam Pahlavan, Sana Sabah Sabry, Foteini Liwicki, Marcus Liwicki

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.

Chatbot Language Modelling +2

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