Papers with Comet
MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing research has focused on providing individual, well-defined types of context in translation, such as the surrounding text or discrete external variables like the speaker’s gender. |
| Approach: | They introduce a novel neural machine translation framework that interprets all context as text. |
| Outcome: | The proposed framework outperforms a baseline that matched the parameters and significantly outperformed it in English translation. |
Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning (2025.coling-main)
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| Challenge: | Existing end-to-end task-oriented dialog systems often encounter challenges arising from implicit information, coreference, and the presence of noisy and irrelevant data within the dialog context. |
| Approach: | They propose a dialog context fusion mechanism for end-to-end task-oriented dialog augmented with three additional tasks: dialog summarization, domain prediction, and slot detection. |
| Outcome: | The proposed method achieves state-of-the-art on the MultiWOZ and CrossWOZ datasets. |