Andrey Savchenko
2024
Leveraging Summarization for Unsupervised Dialogue Topic Segmentation
Aleksei Artemiev
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Daniil Parinov
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Alexey Grishanov
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Ivan Borisov
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Alexey Vasilev
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Daniil Muravetskii
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Aleksey Rezvykh
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Aleksei Goncharov
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Andrey Savchenko
Findings of the Association for Computational Linguistics: NAACL 2024
Traditional approaches to dialogue segmentation perform reasonably well on synthetic or written dialogues but suffer when dealing with spoken, noisy dialogs. In addition, such methods require careful tuning of hyperparameters. We propose to leverage a novel approach that is based on dialogue summaries. Experiments on different datasets showed that the new approach outperforms popular state-of-the-art algorithms in unsupervised topic segmentation and requires less setup.
2020
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements
Andrey Savchenko
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Anton Alekseev
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Sejeong Kwon
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Elena Tutubalina
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Evgeny Myasnikov
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Sergey Nikolenko
Proceedings of the 28th International Conference on Computational Linguistics
Understanding image advertisements is a challenging task, often requiring non-literal interpretation. We argue that standard image-based predictions are insufficient for symbolism prediction. Following the intuition that texts and images are complementary in advertising, we introduce a multimodal ensemble of a state of the art image-based classifier, a classifier based on an object detection architecture, and a fine-tuned language model applied to texts extracted from ads by OCR. The resulting system establishes a new state of the art in symbolism prediction.