Papers by Sebastian Krause
Encode, Tag, Realize: High-Precision Text Editing (D19-1)
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| Challenge: | Neural sequence-to-sequence models provide a powerful framework for learning to translate source texts into target texts. |
| Approach: | They propose a sequence tagging approach that casts text generation as a text editing task. |
| Outcome: | The proposed model outperforms strong seq2seq models on sentence fusion, sentence splitting, abstractive summarization, and grammar correction tasks and achieves state-of-the-art performance. |
Text Generation with Text-Editing Models (2022.naacl-tutorials)
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Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn
| Challenge: | Text-editing models are a popular alternative to seq2seq for monolingual text generation tasks such as text summarization and style transfer. |
| Approach: | They propose to use text-editing models to predict edit operations applied to the source sequence and to generate outputs word-by-word from scratch. |
| Outcome: | This paper provides an overview of the text-edit based models and their current state-of-the-art approaches. |
Automatic Prediction of Discourse Connectives (L18-1)
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| Challenge: | Discourse connectives are used to bind together and explicate the relation between pieces of text. |
| Approach: | They propose to use a dataset of 2.9M sentence pairs separated by discourse connectives to test their accuracy. |
| Outcome: | The proposed model outperforms the human model in the prediction task . the proposed model has a higher F1 under specific conditions . |
A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
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| Challenge: | Modern approaches view the task of Grammatical Error Correction (GEC) as monolingual text-to-text rewriting and employ encoderdecoder neural architectures. |
| Approach: | They propose a language-agnostic method to generate a large number of synthetic examples and use large-scale multilingual language models to train state-of-the-art GEC models. |
| Outcome: | The proposed method surpasses state-of-the-art results on GEC benchmarks in English, Czech, German and Russian. |