Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems (2020.findings-emnlp)
Copied to clipboard
Andrea Madotto, Samuel Cahyawijaya, Genta Indra Winata, Yan Xu, Zihan Liu, Zhaojiang Lin, Pascale Fung
| Challenge: | End-to-end systems rely on dialogue state tracking and annotations to fulfill user requests . modularized systems require multiple steps, including a direct interaction with the KB . |
| Approach: | They propose a method to embed the KB directly into the model parameters . they evaluate five task-oriented dialogue datasets with small, medium, and large KBs . |
| Outcome: | The proposed model can embed the KB directly into the model parameters without any DST or template responses, nor the kb as input. |
Similar Papers
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
Copied to clipboard
| 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. |
| Approach: | They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models. |
| Outcome: | The proposed framework outperforms baseline models on automatic and human evaluation on a Stanford Multi-turn Multi-domain task-oriented dialogue dataset. |
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)
Copied to clipboard
| 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. |
Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent work on end-to-end dialogue models with pre-trained dialogue corpora shows promising performance in the conversational system. |
| Approach: | They propose an end-to-end TOD system with task-optimized adapters which learn independently per task adding only small number of parameters after fixed layers of pre-trained network. |
| Outcome: | The proposed system achieves state-of-the-art performance on the MultiWOZ benchmark compared to existing models. |
PEK: A Parameter-Efficient Framework for Knowledge-Grounded Dialogue Generation (2024.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models have shown great dialogue generation capability in different scenarios, but the huge VRAM consumption when fine-tuning them is one of their drawbacks. |
| Approach: | They propose a parameter-efficient framework for knowledge-enhanced dialogue generation that leverages external knowledge documents and knowledge graphs to enhance its generation capabilities. |
| Outcome: | The proposed framework outperforms baseline methods on multiple evaluation metrics on Wizard of Wikipedia and CMU_DoG datasets. |
GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems (2020.emnlp-main)
Copied to clipboard
| 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. |
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing neural task-oriented dialogue systems cannot be encoded by memory networks, such as memory networks. |
| Approach: | They propose an end-to-end trainable text-to SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries. |
| Outcome: | The proposed method significantly improves on the AirDialogue dataset, which contains the conversations of customers booking flight tickets from the agent. |
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems (P18-1)
Copied to clipboard
| 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. |
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System (2023.emnlp-main)
Copied to clipboard
| Challenge: | generative models struggle to distinguish subtle differences among retrieved knowledge records, resulting in suboptimal quality of generated responses. |
| Approach: | They propose to use maximum marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision. |
| Outcome: | The proposed approach improves on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models. |
Leveraging Explicit Procedural Instructions for Data-Efficient Action Prediction (2023.findings-acl)
Copied to clipboard
| Challenge: | a paper presents a data-efficient solution to constructing task-oriented dialogue systems . large language models have shown success in modeling such dialogues, but they require large quantities of data . |
| Approach: | They propose a system that leverages explicit instructions from agent guidelines . they propose dialogue-document matching and action-oriented masked language modeling . |
| Outcome: | The proposed system improves accuracy predicting in- and out-of-distribution actions while preserving high performance in settings with low or sparse data. |
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent research focused on knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured. |
| Approach: | They propose a novel task-oriented dialogue system that effectively incorporates knowledge into a language model by using structural information of a knowledge graph. |
| Outcome: | The proposed system views relational knowledge as a knowledge graph and introduces (1) a structure-aware knowledge embedding technique, and (2) a Knowledge graph-weighted attention masking strategy to facilitate the system selecting relevant information during the dialogue generation. |