| Challenge: | Recent advances in pretraining methods have achieved promising results on NLP tasks . however, it is unclear which pretraining objective is the most effective for each downstream task . |
| Approach: | They evaluate the effectiveness of domain-adaptive pretraining objectives on downstream tasks . they use open-domain data to pretrain language models like BERT and SpanBERT . |
| Outcome: | The proposed model improves on two dialogue understanding tasks with domain-adaptive pretraining objectives. |
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| Challenge: | Existing methods for pretraining dialog context encoders are still in their infancy. |
| Approach: | They propose to use unsupervised pretraining objectives for dialog context representations to fine-tune and evaluate them on a set of downstream dialog tasks. |
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Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks (2020.acl-main)
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
| Challenge: | Language models prerained on text from a wide variety of sources form the foundation of today’s NLP. |
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Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks (2021.emnlp-main)
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| Challenge: | Pre-trained models can be fine-tuned on domain-specific unlabeled data . however, most further pre-training works just keep running the conventional pre- training task . |
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| Challenge: | Pretraining large language models has resulted in tremendous performance improvement for many natural language processing tasks. |
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Soft Alignment Objectives for Robust Adaptation of Language Generation (2023.acl-long)
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| Challenge: | Domain adaptation is a common approach for generative language models, but it is notorious for over-specialization to the target domain, resulting in catastrophic forgetting. |
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| Challenge: | Existing models that use millions of parameters on massive data are inefficient and lack interpretability. |
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TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue (2020.emnlp-main)
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| Challenge: | Existing pre-trained language models with self-attention encoder architectures are less useful in practice. |
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A Thorough Evaluation of Task-Specific Pretraining for Summarization (2021.emnlp-main)
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| Challenge: | Previous work has used task-agnostic pretraining methods like masked language models or corrupted span prediction to improve performance on downstream tasks. |
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Structural Pre-training for Dialogue Comprehension (2021.acl-long)
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| Challenge: | Recent advances in large-scale pre-training language models (PrLMs) have achieved remarkable successes in a variety of natural language processing tasks. |
| Approach: | They propose to use SPIDER to capture dialogue exclusive features from dialogue texts. |
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Pretrained Language Models for Dialogue Generation with Multiple Input Sources (2020.findings-emnlp)
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| Challenge: | Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. |
| Approach: | They propose to fuse attention information from multiple input sources to achieve better relevance with dialogue history than simple fusion baselines. |
| Outcome: | The proposed models deliver higher relevance with dialogue history than baselines. |