Enhancements to the Sequence-to-Sequence-Based Natural Answer Generation Models

Palasundram, Kulothunkan; Sharef, Nurfadhlina Mohd; Kasmiran, Khairul Azhar; Azman, Azreen

doi:10.1109/access.2020.2978551

Cited by 20 publications

(13 citation statements)

References 62 publications

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“…Seq2seq model has proven to be superior to another neural network models in a wide range of natural language generation tasks [40], including machine translation that is related to our task. In this section, we first describe the definition of the TCM prescription generation, then we introduce how to apply translator model in the prescription prediction, at last we show how to enhance the model to generate more diverse herbs in the specific setting of this task by introducing attention and coverage mechanism.…”

Section: Methodsmentioning

confidence: 98%

TPGen: Prescription Generation Using Knowledge-guided Translator

Ruan¹,

Hua²,

Wu³

et al. 2021

Preprint

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Background: Prescriptions contain a lot of clinical information and play a pivotal role in the clinical diagnosis of Traditional Chinese Medicine (TCM), which is a combination of herb to treat the symptoms of a patient from decision-making of doctors. In the process of clinical decision-making, a large number of prescriptions have been invented and accumulated based on TCM theories. Mining complex and the regular relationships between symptoms and herbs in the prescriptions are important for both clinical practice and novel prescription development. Previous work used several machine learning methods to discover regularities and generate prescriptions but rarely used TCM knowledge to guide prescription generation and described why each herb is predicted for treating a symptom. Methods: In this work, we employ a machine translation mechanism and propose a novel sequence-to-sequence (seq2seq) architecture termed TPGen to generate prescriptions. TPGen consisting of an encoder and a decoder is a well-known framework for resolving the machine translation problem in the natural language processing (NLP) domain. We use the lite transformer and Bi-directional Gate Recurrent Units(Bi-GRUS) as a fundamental model in TPGen, and integrate TCM clinical knowledge to guide the model improvement termed TPGen+. Results: We conduct extensive experiments on a public TCM dataset and clinical data. The experimental results demonstrate that our proposed model is effective and outperforms other state-of-the-art methods in TCM expert evaluation. The approach will be beneficial for clinical prescription discovery and diagnosis

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Section: Methodsmentioning

confidence: 98%

TPGen: Prescription Generation Using Knowledge-guided Translator

Ruan¹,

Hua²,

Wu³

et al. 2021

Preprint

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“…Bu bağlamda, dizi cümledeki kelimelere karşılık gelen semboller listesidir. Yaygın olarak makine çevirisi, diyalog sistemleri, soru cevaplama ve metin özetleme gibi alanlarda büyük başarı elde etmiştir [32]- [34]. Genellikle tekrarlayan bir derin öğrenme ağı ve bir dikkat bileşeni içermektedir.…”

Section: Seq2seq (Seq2seq)unclassified

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

Yigit

Amasyali

2021

Bilişim Teknolojileri Dergisi

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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.

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“…Sequence-to-Sequence learning is a part of ML and a method of neural networks that is mostly utilized in language processing models [17][18][19][20][21][22][23]. It can be implemented with recurrent neural networks (RNNs) using encoder-decoder based machine interpretation that maps an input sequence to a yield of a succession of output sequence with a tag and consideration esteem.…”

Section: Introductionmentioning

confidence: 99%

A systematic review on sequence-to-sequence learning with neural network and its models

Yousuf

Lahzi

Salloum

et al. 2021

IJECE

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We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.

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Enhancements to the Sequence-to-Sequence-Based Natural Answer Generation Models

Cited by 20 publications

References 62 publications

TPGen: Prescription Generation Using Knowledge-guided Translator

TPGen: Prescription Generation Using Knowledge-guided Translator

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

A systematic review on sequence-to-sequence learning with neural network and its models

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