Search Results for author: Prasetya Ajie Utama

Found 7 papers, 4 papers with code

Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning

1 code implementation EMNLP 2021 Prasetya Ajie Utama, Nafise Sadat Moosavi, Victor Sanh, Iryna Gurevych

Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem.

Language Modelling Sentence +1

Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures

no code implementations23 Oct 2020 Nafise Sadat Moosavi, Marcel de Boer, Prasetya Ajie Utama, Iryna Gurevych

Existing approaches to improve robustness against dataset biases mostly focus on changing the training objective so that models learn less from biased examples.

Data Augmentation Sentence

Towards Debiasing NLU Models from Unknown Biases

1 code implementation EMNLP 2020 Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych

Recently proposed debiasing methods are shown to be effective in mitigating this tendency.

Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

1 code implementation ACL 2020 Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych

Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.

Natural Language Understanding

Improving Generalization by Incorporating Coverage in Natural Language Inference

no code implementations19 Sep 2019 Nafise Sadat Moosavi, Prasetya Ajie Utama, Andreas Rücklé, Iryna Gurevych

Finally, we show that using the coverage information is not only beneficial for improving the performance across different datasets of the same task.

Natural Language Inference Relation

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