Papers by Caglar Tirkaz
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems (2020.coling-industry)
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| Challenge: | a number of dialog systems have been developed to perform tasks with high accuracy on benchmarks, but there is a problem with annotated seed data. |
| Approach: | They propose a model that augments initial seed data by paraphrasing existing utterances automatically. |
| Outcome: | The proposed approach improves intent classification and slot labeling on a public dataset and with a real-world dialog system. |
Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances (2020.coling-industry)
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| Challenge: | In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives. |
| Approach: | They propose a method to leverage this feedback by creating annotated training examples from it. |
| Outcome: | The proposed method can be used in a commercial dialog system across various domains and three languages. |