Papers with DMR
Dialogue Meaning Representation for Task-Oriented Dialogue Systems (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on dialogue meaning representations is limited in scalability for complex expressions. |
| Approach: | They propose a pliable and easily extendable representation for task-oriented dialogue . they propose an inheritance hierarchy mechanism focusing on domain extensibility . |
| Outcome: | The proposed representation can be easily extended to a task-oriented dialogue dataset. |
Deep Dirichlet Multinomial Regression (N18-1)
Copied to clipboard
| Challenge: | supervised topic models can incorporate arbitrary document-level features to inform topic priors, but their ability to model corpora is limited by the representation and selection of these features. |
| Approach: | They propose a generative topic model that simultaneously learns document feature representations and topics. |
| Outcome: | The proposed model outperforms DMR and LDA on three datasets and human subjects judge it more representative of associated document features. |
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations (2023.acl-long)
Copied to clipboard
| Challenge: | Discourse markers are natural representations of discourse in our daily language. |
| Approach: | They propose to use unlimited discourse marker data to learn a Distributed Marker Representation by bridging markers with sentence pairs. |
| Outcome: | The proposed model outperforms existing models on the implicit discourse relation recognition task and provides strong interpretability. |
Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for candidate answer extraction are reliant on linguistic rules or annotated data and face partial annotation issue and challenges in generalization. |
| Approach: | They propose an unsupervised approach that leverages the inherent structure of context passages through a Differentiable Masker-Reconstructor (DMR) Model with the enforcement of self-consistency for picking up salient information tokens. |
| Outcome: | The proposed model outperforms supervised and unsupervised methods in two datasets with exhaustively-annotated answers and shows that it is comparable to supervised methods. |