Search Results for author: Daniil Sorokin

Found 10 papers, 7 papers with code

Local-to-global learning for iterative training of production SLU models on new features

no code implementations NAACL (ACL) 2022 Yulia Grishina, Daniil Sorokin

First, we apply the original LGL schedule on our data and then adapt LGL to the production setting where the full data is not available at initial training iterations.

Intent Classification Intent Classification and Slot Filling +1

Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances

no code implementations COLING 2020 Tobias Falke, Markus Boese, Daniil Sorokin, Caglar Tirkaz, Patrick Lehnen

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.

Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems

no code implementations COLING 2020 Shailza Jolly, Tobias Falke, Caglar Tirkaz, Daniil Sorokin

Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling.

Intent Classification Paraphrase Generation

Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning

1 code implementation WS 2018 Teresa Botschen, Daniil Sorokin, Iryna Gurevych

Common-sense argumentative reasoning is a challenging task that requires holistic understanding of the argumentation where external knowledge about the world is hypothesized to play a key role.

Argument Mining Common Sense Reasoning +7

UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification

1 code implementation WS 2018 Andreas Hanselowski, Hao Zhang, Zile Li, Daniil Sorokin, Benjamin Schiller, Claudia Schulz, Iryna Gurevych

The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text.

Claim Verification Entity Linking +2

Context-Aware Representations for Knowledge Base Relation Extraction

1 code implementation EMNLP 2017 Daniil Sorokin, Iryna Gurevych

We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation.

Question Answering Relation Extraction

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