NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation (D18-1)
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| Challenge: | Sequence-to-Sequence models favor short generic responses . however, the model is not suitable for modeling dialogues . |
| Approach: | They propose a model that connects preceding and following conversations to a prior distribution to avoid non-differentiability of discrete natural language tokens. |
| Outcome: | The proposed model is highly efficient in learning the backbone of human-computer communications, but favors short generic responses. |
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| Challenge: | Existing pre-training models for dialogue generation have been proven effective for a wide range of tasks. |
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Extending Neural Generative Conversational Model using External Knowledge Sources (D18-1)
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| Challenge: | Existing generative dialogue models lack coherence and are content poor . however, current models lack the capacity to handle large unstructured knowledge sources. |
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| Challenge: | End-to-end task-oriented dialog systems often suffer from the challenge of incorporating knowledge bases. |
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Cross Copy Network for Dialogue Generation (2020.emnlp-main)
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A Sequence-to-Sequence Approach to Dialogue State Tracking (2021.acl-long)
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| Challenge: | Existing methods for dialogue state tracking are still challenging, but they are improving . a new approach to dialogue state monitoring is proposed, called Seq2Seq-DU . |
| Approach: | They propose a new dialogue state tracking module that formalizes DST as a sequence-to-sequence problem. |
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| Challenge: | generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging. |
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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. |
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Diversifying Dialogue Generation with Non-Conversational Text (2020.acl-main)
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| Challenge: | Neural network-based sequence-to-sequence models suffer from low diversity in open-domain dialogue generation. |
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Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
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| Challenge: | Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base. |
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Text-to-Code Generation with Modality-relative Pre-training (2024.eacl-long)
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| Challenge: | Large pre-trained language models have been applied to programming language tasks with great success, often through further pre-training of a strictly-natural language model. |
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