Papers by Melanie Bradford
Reducing cohort bias in natural language understanding systems with targeted self-training scheme (2023.acl-industry)
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| Challenge: | In deep learning models, it is hard to capture all the variations of the language that different users can use. |
| Approach: | They propose a framework that uses four active learning strategies to identify important samples coming from new users and a self training phase where a teacher model is trained from the first phase to expand the training data with relevant cohort utterances. |
| Outcome: | The proposed framework reduces the bias related to new customers in a digital voice assistant system by using two phases: a fixing phase and a self training phase. |
Semi-supervised Adversarial Text Generation based on Seq2Seq models (2022.emnlp-industry)
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| Challenge: | In contrast, adversarial training has been used in computer vision to improve models’ robustness due to the discrete nature of text. |
| Approach: | They propose a way to generate adversarial samples by using pseudo-labeled in-domain text data to train a seq2seq model for adversarials and combine it with paraphrase detection. |
| Outcome: | The proposed model generates realistic and relevant adversarial samples compared to other state-of-the-art models and recovers up to 70% of errors. |
To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding? (2020.coling-main)
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| Challenge: | Existing models for multilingual SLU are mostly DNN-based joint models of intent classification and slot filling. |
| Approach: | They propose a BERT-based adversarial model architecture to learn language-shared and language-specific representations for multilingual SLU. |
| Outcome: | The proposed model narrows the gap to the ideal multilingual performance. |