Challenge: End-to-end (E2E) automatic speech recognition models struggle to recognize out-of-domain words such as proper nouns and domain-specific terms.
Approach: They propose a domain adaptation technique that relies solely on textual data to adapt to out-of-domain words.
Outcome: The proposed method outperforms the base model by up to 14% relative word error rate improvement on several out-of-domain, publicly available datasets.

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Challenge: Existing approaches to connectionist temporal classification (CTC) are based on pre-trained language models (LMs)
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DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction (2021.emnlp-main)

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Challenge: Existing methods for pre-training can be sub-optimal in some cases . for example, aspect extraction tasks require domain and category invariant representations .
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CTC-based Non-autoregressive Textless Speech-to-Speech Translation (2024.findings-acl)

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Challenge: Existing direct speech-to-speech translation models require text supervision during training, which is not feasible for numerous unwritten languages.
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Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations (2020.coling-main)

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Challenge: Recent advances in deep neural network models have improved parsing performance on in-domain texts . however, the problem is to improve performance on out-of-domain text data when there is only a small-scale out-domain labeled data.
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Type Enhanced BERT for Correcting NER Errors (2023.findings-acl)

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Challenge: Named entity recognition (NER) is the task of identifying spans that belong to particular categories, such as person, location, organization, etc.
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Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains (2021.findings-acl)

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Challenge: Large pre-trained models suffer from domain shift and are not optimal for specific domains.
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Non-Parametric Domain Adaptation for End-to-End Speech Translation (2022.emnlp-main)

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Challenge: End-to-end speech translation (E2E-ST) systems have received increasing attention due to its less error propagation, lower latency and fewer parameters.
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Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification (2020.lrec-1)

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Challenge: Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect Based Sentimence Analysis (ABSA) . recent deep transfer-learning methods have been applied successfully to a myriad of NLP tasks.
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Attentively Embracing Noise for Robust Latent Representation in BERT (2020.coling-main)

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Challenge: Modern digital personal assistants interact with users through voice . high error rates still prevail in the widespread adoption of speech technology .
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BERTwich: Extending BERT’s Capabilities to Model Dialectal and Noisy Text (2023.findings-emnlp)

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Challenge: Pre-trained language models like BERT deteriorate in the face of dialect variation or noise.
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