Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing systems that require extensive labor to process user requests are limited in their reasoning capabilities and require extensive manual effort to design. |
| Approach: | They propose a method that allows a transformer model to walk on a large-scale knowledge graph to generate responses by reasoning over differentiable knowledge graphs. |
| Outcome: | The proposed method allows a transformer model to walk on a large-scale knowledge graph to generate responses. |
Similar Papers
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (P19-1)
Copied to clipboard
| Challenge: | Existing models that use a large-scale knowledge graph to create a conversational reasoning model are domain-agnostic and scalable. |
| Approach: | They propose a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph to introduce engaging and contextually diverse entities and attributes. |
| Outcome: | The proposed model retrieves more natural responses than state-of-the-art models in both in-domain and cross-domain tasks. |
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. |
Graph-Based Knowledge Integration for Question Answering over Dialogue (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches for question answering over dialogue did not consider dialogue structure and background knowledge (e.g., relationships between speakers). |
| Approach: | They propose a method which organizes a dialogue as a "relational graph" and uses edges to represent relationships between entities to encode multi-relations knowledge for reasoning. |
| Outcome: | The proposed method is better at tackling complex questions requiring relational reasoning and defending adversarial attacks with distracting sentences. |
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs (D19-1)
Copied to clipboard
| Challenge: | Existing knowledge-based open domain conversation generation models are limited by the use of unstructured knowledge texts. |
| Approach: | They propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. |
| Outcome: | The proposed system is more explainable and flexible than state-of-the-art models. |
Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer (2021.acl-long)
Copied to clipboard
| Challenge: | Recent Transformer-based models aim to integrate fixed background context into non-task-oriented dialogue systems, but the context length is fixed in these architectures, which restricts how much background or dialogue context can be kept. |
| Approach: | They propose a more concise encoding for background context structured in the form of knowledge graphs by expressing the graph connections through restrictions on the attention weights. |
| Outcome: | The proposed architecture reduces space requirements without negative effects on the precision of reproduction of knowledge and perceived consistency. |
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. |
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. |
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. |
Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to intent prediction are limited in highly specialized fields, such as closed-domain dialogue systems, where context comprehension is of paramount importance. |
| Approach: | They propose a method that uses scenario dialog graphs to model dialogues as sequences of transitions between intents, representing distinct goals or requests. |
| Outcome: | The proposed method significantly advances the field of dialogue systems, providing valuable insights into the effectiveness and potential limitations of the proposed approaches. |
Dialog Generation Using Multi-Turn Reasoning Neural Networks (N18-1)
Copied to clipboard
| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
| Approach: | They propose a generalizable dialog generation approach that adapts multi-turn reasoning to generate responses by taking current conversation session context as a document and current query as 'question' they separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. |
| Outcome: | Experiments on Japanese 10-sentence (5-round) conversation modeling show that multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines. |