Query Intent Classification with Short Sentences in Agglutinative Languages

Deveci, Can; Demirbag, Sedat; Erden, Mustafa; Arslan, Levent M.

doi:10.1109/siu49456.2020.9302231

Cited by 2 publications

(1 citation statement)

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“…There are a few studies which investigate intent detection task for Turkish language. In (Deveci et al, 2020), term frequencyinverse document frequency (TF-IDF) features are employed for the task. In (Dündar et al, 2020), it is concluded that contextual word embeddings from transformers improves the intent detection accuracy compared to the classical machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Data Imbalance Problem in Transformer-Based Intent Detection

Büyük¹,

Erden²,

Arslan³

2022

European Journal of Science and Technology

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There are two major problems when deploying a practical intent detection system for a new customer. First, domain-specific data from the customer could be limited and imbalanced. Additionally, despite different customers might share the same domain, their intent categories might be different from each other. Thus, it might be difficult to combine the datasets collected for different customers into a single and larger one. In this paper, we use class weights in the loss computation to alleviate the data imbalance problem. The class weights are defined inversely proportional to the frequency of the class in the training set in order to give more influence to less observed classes. We also employ a two-pass fine-tuning procedure to utilize the information in different in-domain datasets. Experimental results show that intent detection performance is improved significantly when the weighted loss function is used together with the two-pass transfer learning procedure. The absolute performance improvement in percent detection accuracy is approximately 2% over a transformer-based baseline.

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Section: Introductionmentioning

confidence: 99%