Papers by Berlin Chen
An Effective Pronunciation Assessment Approach Leveraging Hierarchical Transformers and Pre-training Strategies (2024.acl-long)
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Bi-Cheng Yan, Jiun-Ting Li, Yi-Cheng Wang, Hsin Wei Wang, Tien-Hong Lo, Yung-Chang Hsu, Wei-Cheng Chao, Berlin Chen
| Challenge: | Existing attempts to quantify a second language learner’s pronunciation proficiency in a target language often sideline the hierarchy of linguistic units and relatedness among the pronunciation aspects. |
| Approach: | They propose a hierarchical automatic pronunciation assessment method that models the intrinsic structures of an utterance while considering the relatedness among the pronunciation aspects. |
| Outcome: | The proposed method can be used to quantify a second language learner’s pronunciation proficiency in a target language by providing fine-grained feedback with multiple pronunciation aspect scores at various linguistic levels. |
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution (2024.findings-naacl)
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| Challenge: | Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the transcript of a learner’s speech. |
| Approach: | They propose to use metric-based classification and loss re-weighting to model the impact of different SSL-based embedding features on the CEFR score. |
| Outcome: | The proposed model outperforms baselines on the ICNALE benchmark dataset, achieving a significant improvement of more than 10% in CEFR prediction accuracy. |
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition (2024.lrec-main)
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| Challenge: | End-to-end automatic speech recognition systems suffer from mistranscription of domain-specific phrases, such as named entities. |
| Approach: | They propose a named entity correction model that leverages phonetic con-fusion to mitigate phonetic confusion. |
| Outcome: | The proposed model outperforms the existing model on AISHELL-1 and Homophone datasets. |
Towards Efficient and Multifaceted Computer-assisted Pronunciation Training Leveraging Hierarchical Selective State Space Model and Decoupled Cross-entropy Loss (2025.naacl-long)
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| Challenge: | APA and MDD are two of the main tasks of computer-assisted pronunciation training (CAPT) systems. |
| Approach: | They propose a computer-assisted pronunciation training approach that integrates APA and MDD tasks in parallel. |
| Outcome: | The proposed approach improves on APA and MDD tasks, and achieves an F1 score of 63.85%. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |