This signal can then be used for downstream modelling and signal identification for commodity trading. We find that fine-tuned BERT models outperform fine-tuned or vanilla GPT models on this task. Transformer models have revolutionized the field of natural language processing (NLP) in recent years,...
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking (Interspeech2021) ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues (EMNLP2020) Conversations Are Not Flat: Modeling the Dynamic In...
Pre-trained Models for Natural Language Processing: A Survey A Survey on Contextual Embeddings A Survey on Transfer Learning in Natural Language Processing Downstream task QA, MC, Dialogue Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond ...
This process ultimately resulted in the development of 423 distinct templates (manual and ChatGPT combined), each consisting of a single sentence. 3.3. Labeled Synthetic Dataset Generation for Training The developed templates were used to construct meaningful sentences by randomly replacing placeholders wi...
ELMo can produce context-sensitive embeddings for each word within a sentence, which can then be supplied to downstream tasks. BERT, and GPT, on the other hand, utilizes a fine-tuning approach that can adapt the entire language model to a downstream task, resulting in a task-specific architec...
Common pretraining models include the generative pretrained trans‑ former (GPT), BERT, enhanced representation through knowledge integration (ERNIE), etc. The current popular BERT model works from the encoder of the bidirectional trans‑ former model [25,26]. We choose the BERT‑wwm model ...
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking (Interspeech2021) ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues (EMNLP2020) Conversations Are Not Flat: Modeling the Dynamic In...