Yazar "Dalyan, Tugba" seçeneğine göre listele

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    A comparative study of neural machine translation models for Turkish language
    (Ios Press, 2022) Ozdemir, Ozgur; Akin, Emre Salih; Velioglu, Riza; Dalyan, Tugba
    Machine translation (MT) is an important challenge in the fields of Computational Linguistics. In this study, we conducted neural machine translation (NMT) experiments on two different architectures. First, Sequence to Sequence (Seq2Seq) architecture along with a variation that utilizes attention mechanism is performed on translation task. Second, an architecture that is fully based on the self-attention mechanism, namely Transformer, is employed to perform a comprehensive comparison. Besides, the contribution of employing Byte Pair Encoding (BPE) and Gumbel Softmax distributions are examined for both architectures. The experiments are conducted on two different datasets: TED Talks that is one of the popular benchmark datasets for NMT especially among morphologically rich languages like Turkish and WMT18 News dataset that is provided by The Third Conference on Machine Translation (WMT) for shared tasks on various aspects of machine translation. The evaluation of Turkish-to-English translations' results demonstrate that the Transformer model with combination of BPE and Gumbel Softmax achieved 22.4 BLEU score on TED Talks and 38.7 BLUE score on WMT18 News dataset. The empirical results support that using Gumbel Softmax distribution improves the quality of translations for both architectures.
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    EMRES: A New EMotional RESpondent Robot
    (IEEE-Inst Electrical Electronics Engineers Inc, 2022) Sonmez, Elena Battini; Han, Hasan; Karadeniz, Oguzcan; Dalyan, Tugba; Sarioglu, Baykal
    The aim of this work is to design an artificial empathetic system and to implement it into an EMotional RESpondent (EMRES) robot, called EMRES. Rather than mimic the expression detected in the human partner, the proposed system achieves a coherent and consistent emotional trajectory resulting in a more credible human-agent interaction. Inspired by developmental robotics theory, EMRES has an internal state and a mood, which contribute in the evolution of the flow of emotions; at every episode, the next emotional state of the agent is affected by its internal state, mood, current emotion, and the expression read in the human partner. As a result, EMRES does not imitate, but it synchronizes to the emotion expressed by the human companion. The agent has been trained to recognize expressive faces of the FER2013 database and it is capable of achieving 78.3% performance with wild images. Our first prototype has been implemented into a robot, which has been created for this purpose. An empirical study run with university students judged in a positive way the newly proposed artificial empathetic system.
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    Interval-Valued Pythagorean Fuzzy AHP&TOPSIS for ERP Software Selection
    (Springer International Publishing Ag, 2022) Dalyan, Tugba; Otay, Irem; Gulada, Mehmet
    The selection of an Enterprise Resource Planning (ERP) system is considerably important since it has effects on the productivity of companies. This paper aims to choose the best decision for ERP software over many conflicting criteria by using Pythagorean fuzzy (PF) Analytic Hierarchy Process (AHP) that integrated with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The proposed model includes a hierarchical structure with four main criteria which are finance, technology, usability and corporate, twenty two sub-criteria and several alternatives. In this study, the evaluations are expressed using interval-valued PF sets that consist of membership and non-membership values, where the square sum of these degrees is at most 1. The weights of the criteria are computed using interval-valued PF AHP. Then, fuzzy TOPSIS method is utilized to evaluate alternatives by considering distances of alternatives to negative and positive ideal solutions (NIS, PIS), respectively. This attempt using interval-valued PF AHP-TOPSIS is deemed to be a significant contribution toward ERP selection problem.
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    Unified benchmark for zero-shot Turkish text classification
    (Elsevier Sci Ltd, 2023) celik, Emrecan; Dalyan, Tugba
    Effective learning schemes such as fine-tuning, zero-shot, and few-shot learning, have been widely used to obtain considerable performance with only a handful of annotated training data. In this paper, we presented a unified benchmark to facilitate the problem of zeroshot text classification in Turkish. For this purpose, we evaluated three methods, namely, Natural Language Inference, Next Sentence Prediction and our proposed model that is based on Masked Language Modeling and pre-trained word embeddings on nine Turkish datasets for three main categories: topic, sentiment, and emotion. We used pre-trained Turkish monolingual and multilingual transformer models which can be listed as BERT, ConvBERT, DistilBERT and mBERT. The results showed that ConvBERT with the NLI method yields the best results with 79% and outperforms previously used multilingual XLM-RoBERTa model by 19.6%. The study contributes to the literature using different and unattempted transformer models for Turkish and showing improvement of zero-shot text classification performance for monolingual models over multilingual models.