Papers by Eetu Sjöblom

2 papers
Modeling Noise in Paraphrase Detection (2022.lrec-1)

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Challenge: Noisy labels in training data are challenging and can lead to incorrect decisions . large pre-trained language models have achieved great results in many NLP tasks .
Approach: They propose to use a linear noise model to augment pre-trained language models to account for label noise in fine-tuning.
Outcome: The proposed model can be applied without further knowledge about annotation quality and label confidence of training examples and their results are compared with other models.
Paraphrase Generation and Evaluation on Colloquial-Style Sentences (2020.lrec-1)

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Challenge: a new study investigates the quality and novelty of generated paraphrases . paraphrase models can be used for information retrieval and data mining .
Approach: They use state-of-the-art neural machine translation models trained on the Opusparcus corpus to generate paraphrases in six languages.
Outcome: The proposed model outperforms the existing model on human evaluation in five of the six languages.

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