Papers with dialog modeling
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management (2021.findings-acl)
Copied to clipboard
| Challenge: | Task-oriented dialog systems typically manage structured knowledge to guide goal-oriented conversations. |
| Approach: | They propose a TOD system with hybrid knowledge management, HyKnow, which extends the belief state to manage both structured and unstructured knowledge. |
| Outcome: | The proposed model outperforms existing TOD systems in the evaluation of a multiWOZ dataset on unstructured knowledge with strong end-to-end performance. |
Frugal Prompting for Dialog Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) are used in natural language processing tasks with an unrealistic speed and effectiveness. |
| Approach: | They propose more compact ways of providing dialog history information while ensuring good performance and reducing model’s inference-API costs. |
| Outcome: | The proposed models have the optimal usable-information density while maintaining good performance and reducing model’s inference-API costs. |
Triple-Encoders: Representations That Fire Together, Wire Together (2024.acl-long)
Copied to clipboard
| Challenge: | Existing search-based dialog models re-encode the dialog history at every turn, causing high cost. |
| Approach: | They propose a method that encodes relative distances between utterances via a bi-encoder . they use a hebbian-inspired co-occurrence learning objective to compute distributed utterrance mixtures . |
| Outcome: | The proposed method improves on bi-encoded utterances and even better generalization . it can encode 2020 and Erker et al. (2023) with the same results. |
Investigating Robustness of Dialog Models to Popular Figurative Language Constructs (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing dialog models are unable to handle popular figurative language constructs like metaphor and simile when faced with dialog contexts containing figurativ language. |
| Approach: | They propose lightweight solutions to help existing dialog models become more robust to figurative language by simply using an external resource to translate figurativ language to literal (non-figurative) forms while preserving the meaning to the best extent possible. |
| Outcome: | The proposed models show large drops in performance when faced with dialog contexts consisting of figurative language compared to contexts without figurativ language . |
Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding (2020.emnlp-main)
Copied to clipboard
| Challenge: | Discourse analysis has been limited to small news corpora, but this study is expanding to tens of thousands of interviews. |
| Approach: | They propose a large-scale analysis of discourse in media dialog and its impact on dialog modeling with a focus on interrogative patterns and use of external knowledge. |
| Outcome: | The proposed model outperforms strong discourse-agnostic baselines for dialog modeling, generating more specific and topical responses in interview-style conversations. |