| Challenge: | Existing models to integrate external Knowledge Base information, one form of world knowledge, confound dialog history with KB tuples and store them into one memory. |
| Approach: | They propose a working memory model that interacts with two long-term memories to generate dialog responses. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two task-oriented dialog datasets. |
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Multi-Level Memory for Task Oriented Dialogs (N19-1)
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| Challenge: | Recent task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. |
| Approach: | They propose a novel multi-level memory architecture that separates dialog context and knowledge base results . they use cells for each query and their corresponding results to address queries . |
| Outcome: | The proposed architecture outperforms current state-of-the-art models on three publicly available data sets. |
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)
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| Challenge: | Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. |
| Approach: | They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history . |
| Outcome: | The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation. |
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems (P18-1)
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| Challenge: | End-to-end task-oriented dialog systems often suffer from the challenge of incorporating knowledge bases. |
| Approach: | They propose a novel yet simple end-to-end differentiable model called memory-tosequence to address this issue. |
| Outcome: | The proposed model can be trained faster and achieve state-of-the-art performance on three different task-oriented dialog datasets. |
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks (D19-1)
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| Challenge: | Existing memory networks do not perform well when leveraging heterogeneous information from different sources. |
| Approach: | They propose to use user utterances, dialogue history and background knowledge tuples to integrate external knowledge into a neural dialogue model. |
| Outcome: | The proposed model outperforms the state-of-the-art data-driven task-oriented dialogue models on real-world datasets. |
End-to-End Learning of Task-Oriented Dialogs (N18-4)
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| Challenge: | Dissertation addresses the limitations of conventional task-oriented dialog systems . conventions of such systems include a complex pipeline and dialog state tracking . |
| Approach: | They propose a neural network based dialog system that can robustly track dialog state . they propose offline training and online interactive learning methods to improve efficiency . |
| Outcome: | The proposed system can track dialog state, interface with knowledge bases, and integrate structured query results into system responses to successfully complete task-oriented dialog. |
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)
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| Challenge: | Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other. |
| Approach: | They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history . |
| Outcome: | The proposed framework outperforms baseline models and produces entity-consistent responses. |
GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems (2020.emnlp-main)
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| Challenge: | End-to-end task-oriented dialogue systems aim to generate system responses directly from plain text inputs. |
| Approach: | They propose a recurrent cell architecture which exploits the structural information in dialogue history . they propose recursive cell architecture to allow representation learning on graphs . |
| Outcome: | The proposed model improves on two different datasets on task-oriented dialogues. |
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog (2023.findings-acl)
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| Challenge: | Empirical results show dialog context representations are more conducive to multi-turn task-oriented dialog. |
| Approach: | They propose an end-to-end task-oriented dialog system with two contrastive learning strategies to model relationship between dialog context and belief/action state representations. |
| Outcome: | Empirical results show that dialog context representations are more conducive to multi-turn task-oriented dialog. |
Modeling Long Context for Task-Oriented Dialogue State Generation (2020.acl-main)
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| Challenge: | Existing approaches to dialogue state tracking are limited to scenarios with infinite slot values and prediction of unseen slot values. |
| Approach: | They propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. |
| Outcome: | The proposed model achieves state-of-the-art accuracy on the MultiWOZ 2.0 dataset. |
Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog (2023.acl-long)
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| Challenge: | Existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses. |
| Approach: | They propose a multi-grained knowledge retrieval system that decouples knowledge retrievals from response generation and introduces an entity selector and an attribute selector to acquire multigrained information from the knowledge base. |
| Outcome: | The proposed system performs better on small and large knowledge bases. |