Papers by David Thulke
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition (2021.acl-srw)
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| Challenge: | Existing methods for named entity recognition use only a limited number of samples . data augmentation and selftraining are popular methods to generate additional synthetic data . |
| Approach: | They investigate the impact of data augmentation and data augmented on named entity recognition tasks. |
| Outcome: | The proposed methods improve the performance of three named entity recognition tasks. |
Mask More and Mask Later: Efficient Pre-training of Masked Language Models by Disentangling the [MASK] Token (2022.findings-emnlp)
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| Challenge: | Large-scale pre-trained MLMs can be used to generalize well to a wide range of tasks. |
| Approach: | They propose to append [MASK]s at a later layer to reduce sequence length for earlier layers. |
| Outcome: | The proposed method outperforms RoBERTa for 6 out of 8 GLUE tasks on average by 0.4%. |
Does Joint Training Really Help Cascaded Speech Translation? (2022.emnlp-main)
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| Challenge: | Currently, in speech translation, the straightforward approach delivers state-of-the-art results, but fundamental challenges such as error propagation remain. |
| Approach: | They propose to combine a cascaded recognition system with a machine translation system to improve cascade speech translation. |
| Outcome: | The proposed methods can improve cascaded speech translation and suggest alternative training methods. |
Medical Spoken Named Entity Recognition (2025.naacl-industry)
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Khai Le-Duc, David Thulke, Hung-Phong Tran, Long Vo-Dang, Khai-Nguyen Nguyen, Truong-Son Hy, Ralf Schlüter
| Challenge: | Named Entity Recognition (NER) aims to extract named entities from speech and categorise them into types like person, location, organization, etc. |
| Approach: | They present a spoken NER dataset in the medical domain using pre-trained models that are encoder-only and sequence-to-sequence. |
| Outcome: | The dataset is the largest spoken NER dataset in the world regarding the number of entity types, featuring 18 distinct types. |
Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model (2022.findings-emnlp)
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| Challenge: | Recent document-grounded dialog systems have seen an increase in popularity. |
| Approach: | They propose a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes’ theorem and propose different approximate decoding schemes. |
| Outcome: | The proposed model is more factual in terms of automatic factuality metrics than the baseline model and can be combined with a recently proposed method to control factuity in grounded dialog, CTRL. |