Papers by Dmitri Roussinov
Recognizing Semantic Relations by Combining Transformers and Fully Connected Models (2020.lrec-1)
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| Challenge: | Current approaches to recognizing semantic relations between words are limited and require a word-path model. |
| Approach: | They propose a distributional approach that is based on an attention-based transformer and a word path model that combines useful properties of a convolutional network with a fully connected language model. |
| Outcome: | The proposed model outperforms the state-of-the-art in terms of performance and data sources. |
BERT Goes Off-Topic: Investigating the Domain Transfer Challenge using Genre Classification (2023.findings-emnlp)
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| Challenge: | Pretrained language models have improved performance of text classification tasks, but they still suffer from spurious domain-specific clues. |
| Approach: | They propose a method to augment pretrained language models by generating texts in any desired genre and on any desired topic. |
| Outcome: | The proposed method improves on genre classification tasks while showing no improvement for other topics. |
Controlling Out-of-Domain Gaps in LLMs for Genre Classification and Generated Text Detection (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have pushed the boundaries of natural language processing, but their consistency is often limited when applied to unfamiliar domains. |
| Approach: | They propose a method that controls which predictive indicators are used and which are excluded during classification. |
| Outcome: | The proposed method reduces the OOD gap by up to 20 percentage points in a few-shot setup. |