Improvement of Intent Classification Using Diacritic Restoration for Text Message in Chatbot

Nguyen, Trang; Shcherbakov, Maxim

doi:10.1007/978-3-030-87034-8_9

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…The statistical language model can be created not only on the word level but on the character level, as in [21]. During the first stage, for recognized words, it uses a statistical n-gram language model with n = [1,4] that works on the word level; during the second stage, it processes the out-of-vocabulary words with the statistical n-gram character-based model that works on the character level. The authors proved that their offered approach led to the better diacritization accuracy of the Arabic dialectal texts.…”

Section: Statistics-based Approachesmentioning

confidence: 99%

“…The diacritics restoration also resulted in a better text-to-speech performance for Romanians [3]. Used as the integrative NLU component, the diacritics restoration also improved the accuracy of the intent classification-based Vietnamese dialogue system [4,5]. Similarly, statistical machine translation performance was positively correlated with correctly diacritized words for Arabic [6].…”

Section: Introductionmentioning

confidence: 99%

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Correcting Diacritics and Typos with a ByT5 Transformer Model

et al. 2022

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Due to the fast pace of life and online communications and the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing in other languages. Restoring diacritics and correcting spelling is important for proper language use and the disambiguation of texts for both humans and downstream algorithms. However, both of these problems are typically addressed separately: the state-of-the-art diacritics restoration methods do not tolerate other typos, but classical spellcheckers also cannot deal adequately with all the diacritics missing.In this work, we tackle both problems at once by employing the newly-developed universal ByT5 byte-level seq2seq transformer model that requires no language-specific model structures. For a comparison, we perform diacritics restoration on benchmark datasets of 12 languages, with the addition of Lithuanian. The experimental investigation proves that our approach is able to achieve results (>98%) comparable to the previous state-of-the-art, despite being trained less and on fewer data. Our approach is also able to restore diacritics in words not seen during training with >76% accuracy. Our simultaneous diacritics restoration and typos correction approach reaches >94% alpha-word accuracy on the 13 languages. It has no direct competitors and strongly outperforms classical spell-checking or dictionary-based approaches. We also demonstrate all the accuracies to further improve with more training. Taken together, this shows the great real-world application potential of our suggested methods to more data, languages, and error classes.

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Section: Statistics-based Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correcting Diacritics and Typos with a ByT5 Transformer Model

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Diacritics restoration also resulted in better text-to-speech performance for Romanian [3]. Used as the integrative NLU component, diacritics restoration also improved the accuracy of the intent classification-based Vietnamese dialogue system [4,5]. Besides, statistical machine translation performance was positively correlated with correctly diacritized words for Arabic [6].…”

Section: Introductionmentioning

confidence: 99%

“…The statistical language model can be created not only on the word level but on the character level as in [21]. During the first stage, for recognized words it uses a statistical n-gram language model with n = [1,4] that works on the word level; during the second stage, it processes out-of-vocabulary words with the statistical n-gram character-based model that works on the character level. The authors prove that their offered approach leads to the better diacritization accuracy of the Arabic dialectal texts.…”

Section: Introductionmentioning

confidence: 99%

Correcting diacritics and typos with a ByT5 transformer model

Stankevičius,

Lukoševičius,

Kapočiūtė-Dzikienė

et al. 2022

Preprint

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Due to the fast pace of life and online communications, the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing. Restoring diacritics and correcting spelling is important for proper language use and disambiguation of texts for both humans and downstream algorithms. However, both of these problems are typically addressed separately, i.e., state-of-the-art diacritics restoration methods do not tolerate other typos. In this work, we tackle both problems at once by employing newly-developed ByT5 byte-level transformer models. Our simultaneous diacritics restoration and typos correction approach demonstrates near state-of-the-art performance in 13 languages, reaching >96% of the alpha-word accuracy. We also perform diacritics restoration alone on 12 benchmark datasets with the additional one for the Lithuanian language. The experimental investigation proves that our approach is able to achieve comparable results (>98%) to previously reported despite being trained on fewer data. Our approach is also able to restore diacritics in words not seen during training with >76% accuracy. We also show the accuracies to further improve with longer training. All this shows a great real-world application potential of our suggested methods to more data, languages, and error classes.

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Arabic Dataset for Farmers' Intent Identification Toward Developing a Chatbot

Elsayed¹,

Ellakwa²

2022

IJCSIT

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A chatbot is an application of artificial intelligence in natural language processing and speech recognition. It is a computer program that imitates humans in making conversations with other people. Chatbots that specialize in a single topic, such as agriculture, are known as domain-specific chatbots. In this paper, we present a dataset for farmer intents. Intent identification is the first step in building a chatbot. The dataset includes five intents (pest or disease identification, irrigation, fertilization, weed identification, and plantation date). The length of the dataset is 720 records. We applied a Multi-Layers Perceptron (MLP) for intent classification. We tried different numbers of neurons per hidden layer and compared between increasing the number of neurons with the fixed number of epochs. The result shows that as the number of neurons in the hidden layers increases, the introduced MLP achieves high accuracy in a small number of epochs. MLP achieves 97% accuracy on the introduced dataset when the number of neurons in each hidden layer is 256 and the number of epochs is 10.

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Improvement of Intent Classification Using Diacritic Restoration for Text Message in Chatbot

Cited by 3 publications

References 16 publications

Correcting Diacritics and Typos with a ByT5 Transformer Model

Correcting Diacritics and Typos with a ByT5 Transformer Model

Correcting diacritics and typos with a ByT5 transformer model

Arabic Dataset for Farmers' Intent Identification Toward Developing a Chatbot

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