Papers with response generation tasks
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking. |
| Approach: | They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation. |
| Outcome: | The proposed architecture improves the integration of recommendation and dialog generation functions. |
CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for generating emotion-controllable response are inadequate due to content consistency and lack of coherence. |
| Approach: | They propose a framework that extends the emotion-controllable response generation to a dual task to generate emotional responses and emotional queries alternatively. |
| Outcome: | The proposed framework outperforms baseline models in coherence, diversity, and relation to emotion factors. |
Semantic Representation for Dialogue Modeling (2021.acl-long)
Copied to clipboard
| Challenge: | Existing models for dialogue modeling lack ability to represent core semantics, such as ignoring important entities. |
| Approach: | They develop an algorithm to construct dialogue-level AMR graphs from sentence-level data and explore two ways to incorporate AMRs into dialogue modeling. |
| Outcome: | The proposed model is superior to existing models on dialogue understanding and response generation tasks. |
TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for Task-Oriented Dialogs (TOD) lack consideration for adaptive response styles and neglect to simulate interactions with app contexts like calendars or alarms. |
| Approach: | They propose to generate an annotated task-oriented dialog dataset and an automatic pipeline to generate it. |
| Outcome: | The proposed dataset provides a variety of system response styles and provides verbose or non-verbal responses. |