Papers with in-domain corpus
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis (2020.findings-emnlp)
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| Challenge: | Recent studies show that learning domain-specific language models are equally important for general-purpose and domain-based learning. |
| Approach: | They propose a domain-oriented learning task that combine the benefits of both general and domain-specific worlds. |
| Outcome: | The proposed task solves the problems in an aspect-based sentiment analysis task. |
Lifelong Learning of Topics and Domain-Specific Word Embeddings (2021.findings-acl)
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| Challenge: | Existing lifelong topic models focus on indomain text streams in which each chunk only contains documents from a single domain. |
| Approach: | They develop a lifelong collaborative model that uses non-negative matrix factorization to learn topics and domain-specific word embeddings. |
| Outcome: | The proposed model can learn topics and domain-specific word embeddings from a lifelong collaborative model. |
Continual Learning for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Neural machine translation models are data-driven and require large-scale training corpus . continual learning remains a big challenge for artificial intelligence systems and models . |
| Approach: | They propose a continual learning framework for NMT models that incorporates multiple stages of training to alleviate catastrophic forgetting problem. |
| Outcome: | The proposed framework achieves superior performance compared to baseline models in all settings. |