Soru Cevaplama Sistemleri Üzerine Detaylı Bir Çalışma: Veri Kümeleri, Yöntemler ve Açık Araştırma Alanları

Soru Cevaplama (QA) sistemleri, kullanıcıların doğal dilde sordukları sorulara belge veya bağlantıları listelemek yerine doğrudan cevap almalarını sağlayan sistemlerdir. Bu çalışmada, QA sistemlerinde yaygın kullanılan veri kümeleri tanıtılmış ve çeşitli özelliklere göre karşılaştırılmıştır. Ayrıca, QA alanındaki diğer çalışmalardan farklı olarak bu çalışmada son yıllarda literatürde yer alan QA sistemlerinin arkasında kullanılan yöntemlere odaklanılmıştır. Bu yöntemler dört farklı grupta ele alınmış olup literatürdeki güncel çalışmaları ve teknolojileri içermektedir. Bu modeller kullanılan teknikler, harici bilgi kaynaklarının veya dil modelinin kullanılıp kullanılmadığı gibi faktörlere göre karşılaştırılmıştır. Dikkat mekanizmasının, dil modellerinin, çizge işleyen ağların, harici bilgi kaynaklarının, kolektif öğrenmenin ve derin öğrenme mimarilerinin QA sistemlerinin başarısı üzerinde genel olarak olumlu etkisi olduğu görülmüştür. Ayrıca, bu çalışmada QA sistemlerinin günümüzdeki açık araştırma alanları ve olası çözüm yolları belirlenerek gelecekteki QA sistemleri için önerilerde bulunulmuştur. Gelecekteki araştırma alanları olarak yeterli veriye sahip olmayan diller üzerindeki sistemler, birden fazla dil üzerinde çalışabilen sistemler, çok sayıda bilgi kaynağının kullanılmasının gerekli olduğu sistemler ve karşılıklı konuşmaya dayalı sistemler öne çıkmaktadır.

A Comprehensive Study on Question Answering Systems: Datasets, Methods and Open Research Areas

Question Answering (QA) systems allow users to get direct answers to questions they ask in natural language instead of listing documents or links. In this study, current QA datasets are introduced and compared according to various properties. Unlike other studies in QA, this study focuses on the methods used in current QA systems. These methods are discussed in four different categories and include recent studies and technologies. The models are compared with various factors such as techniques used, external knowledge, or language model. In general, attention mechanisms, language models, graph neural networks, external knowledge, collective learning, and deep learning architectures positively affect the success of QA systems. In addition, current open research areas of QA systems and possible solutions are determined, and suggestions for future QA systems are given. Systems on languages that do not have enough data, systems that can work on more than one language, systems that require the use of many information sources, and speech-based systems stand out as future research areas.

Keywords:

question answering, deep learning, knowledge bases memory networks, seq2seq,

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