Malamas Nikolas

Nikolaos Malamas, Konstantinos Papangelou and Andreas L. Symeonidis

Healthcare, 10, (1), 2022 Jan

Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed “healthcare vocabulary” to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor’s appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant’s behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.

@article{malamas-healthcare,
author={Nikolaos Malamas and Konstantinos Papangelou and Andreas L. Symeonidis},
title={Upon Improving the Performance of Localized Healthcare Virtual Assistants},
journal={Healthcare},
volume={10},
number={1},
year={2022},
month={01},
date={2022-01-04},
url={https://www.mdpi.com/2227-9032/10/1/99},
doi={https://doi.org/10.3390/healthcare10010099},
issn={2227-9032},
keywords={chatbot; virtual assistant; Rasa; ehealthcare},
abstract={Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed “healthcare vocabulary” to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor’s appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant’s behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.}
}

Andreas Goulas, Nikolaos Malamas and Andreas L. Symeonidis

Natural Language Processing and Information Systems, pp. 197--208, Springer International Publishing, Cham, 2022 Jun

Conversational assistants with increasing NLP capabilities are becoming commodity functionality for most new devices. However, the underlying language models responsible for language-related intelligence are typically characterized by a large number of parameters and high demand for memory and resources. This makes them a no-go for edge and low-resource devices, forcing them to be cloud-hosted, hence experiencing delays. To this end, we design a systematic language-agnostic methodology to develop powerful lightweight NLP models using knowledge distillation techniques, this way building models suitable for such low resource devices. We follow the steps of the proposed approach for the Greek language and build the first - to the best of our knowledge - lightweight Greek language model, which we make publicly available. We train and evaluate GloVe word embeddings in Greek and efficiently distill Greek-BERT into various BiLSTM models, without considerable loss in performance. Experiments indicate that knowledge distillation and data augmentation can improve the performance of simple BiLSTM models for two NLP tasks in Modern Greek, i.e., Topic Classification and Natural Language Inference, making them suitable candidates for low-resource devices.

@inproceedings{goulas-et-al,
author={Andreas Goulas and Nikolaos Malamas and Andreas L. Symeonidis},
title={A Methodology for Enabling NLP Capabilities on Edge and Low-Resource Devices},
booktitle={Natural Language Processing and Information Systems},
pages={197--208},
publisher={Springer International Publishing},
address={Cham},
year={2022},
month={06},
date={2022-06-13},
url={https://link.springer.com/chapter/10.1007/978-3-031-08473-7_18},
doi={https://doi.org/10.1007/978-3-031-08473-7_18},
isbn={978-3-031-08473-7},
keywords={Natural language processing;Knowledge distillation;Word embeddings;Lightweight models},
abstract={Conversational assistants with increasing NLP capabilities are becoming commodity functionality for most new devices. However, the underlying language models responsible for language-related intelligence are typically characterized by a large number of parameters and high demand for memory and resources. This makes them a no-go for edge and low-resource devices, forcing them to be cloud-hosted, hence experiencing delays. To this end, we design a systematic language-agnostic methodology to develop powerful lightweight NLP models using knowledge distillation techniques, this way building models suitable for such low resource devices. We follow the steps of the proposed approach for the Greek language and build the first - to the best of our knowledge - lightweight Greek language model, which we make publicly available. We train and evaluate GloVe word embeddings in Greek and efficiently distill Greek-BERT into various BiLSTM models, without considerable loss in performance. Experiments indicate that knowledge distillation and data augmentation can improve the performance of simple BiLSTM models for two NLP tasks in Modern Greek, i.e., Topic Classification and Natural Language Inference, making them suitable candidates for low-resource devices.}
}

Nikolaos Malamas and Andreas Symeonidis

Procedia Computer Science, 192, pp. 109-118, 2021 Jan

Over the past few years, there has been a boost in the use of commercial virtual assistants. Obviously, these proprietary tools are well-performing, however the functionality they offer is limited, users are ”vendor-locked”, while possible user privacy issues rise. In this paper we argue that low-cost, open hardware solutions may also perform well, given the proper setup. Specifically, we perform an initial assessment of a low-cost virtual agent employing the Rasa framework integrated into a Raspberry Pi 4. We set up three different architectures, discuss their capabilities and limitations and evaluate the dialogue system against three axes: assistant comprehension, task success and assistant usability. Our experiments show that our low-cost virtual assistant performs in a satisfactory manner, even when a small-sized training dataset is used.

@article{malamas2021-rasa,
author={Nikolaos Malamas and Andreas Symeonidis},
title={Embedding Rasa in edge Devices: Capabilities and Limitations},
journal={Procedia Computer Science},
volume={192},
pages={109-118},
year={2021},
month={01},
date={2021-01-01},
url={https://www.sciencedirect.com/science/article/pii/S187705092101499X},
doi={https://doi.org/10.1016/j.procs.2021.08.012},
issn={1877-0509},
keywords={Spoken Dialogue Systems;NLU;Rasa;Chatbots},
abstract={Over the past few years, there has been a boost in the use of commercial virtual assistants. Obviously, these proprietary tools are well-performing, however the functionality they offer is limited, users are ”vendor-locked”, while possible user privacy issues rise. In this paper we argue that low-cost, open hardware solutions may also perform well, given the proper setup. Specifically, we perform an initial assessment of a low-cost virtual agent employing the Rasa framework integrated into a Raspberry Pi 4. We set up three different architectures, discuss their capabilities and limitations and evaluate the dialogue system against three axes: assistant comprehension, task success and assistant usability. Our experiments show that our low-cost virtual assistant performs in a satisfactory manner, even when a small-sized training dataset is used.}
}