Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue (2023.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge-grounded dialogue systems can generate informative responses based on dialogue history and external knowledge source. |
| Approach: | They conduct a thorough experiment to determine the optimal knowledge form, mutual effects between knowl- edge and model selection, and the few-shot performance of knowledge. |
| Outcome: | The proposed method combines knowledge-grounded dialogue with human-generated dialogues to generate informative and meaningful responses. |
Similar Papers
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs (D19-1)
Copied to clipboard
| Challenge: | Existing work has not shown that knowledge-grounded models can zero-shot adapt to updated, unseen knowledge graphs. |
| Approach: | They propose a task to apply dynamic knowledge graphs to neural conversation models . they propose 'dyKgChat' that selects an output from two networks at each time step . |
| Outcome: | The proposed model outperforms existing knowledge-grounded conversation models in evaluation metrics. |
Is a Knowledge-based Response Engaging?: An Analysis on Knowledge-Grounded Dialogue with Information Source Annotation (2023.acl-srw)
Copied to clipboard
| Challenge: | Currently, most knowledge-grounded dialogue models focus on reflecting given external knowledge. |
| Approach: | They analyze human behavior by annotating utterances in an existing knowledge-grounded dialogue corpus and find that speaker-derived information improves dialogue engagingness. |
| Outcome: | The proposed model cannot include speaker-derived information as often as humans do. |
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. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing conversation models treat knowledge selection as a sentence ranking problem where each sentence is handled individually, ignoring the internal semantic connection between sentences. |
| Approach: | They propose to automatically convert background knowledge documents into document semantic graphs and perform knowledge selection over such graphs. |
| Outcome: | The proposed model improves on the knowledge selection task and the response generation task on HollE and generalizes on unseen topics in WoW. |
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge grounding models focus on locating knowledge in document contexts that are relevant to the conversation. |
| Approach: | They propose a knowledge identification model that leverages document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. |
| Outcome: | The proposed model can be applied to document-grounded conversational datasets and shows generalization to unseen documents and long dialogue contexts. |
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. |
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue. |
| Approach: | They propose to use a multi-reference dataset to assess the one-to-many efficacy of existing KGC models. |
| Outcome: | The proposed model improves the mapping relationship between multiple knowledge and multiple responses by optimizing the model in a wake-sleep style. |
A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022.aacl-main)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable. |
| Approach: | They analyze 507 papers on knowledge graphs in natural language processing (NLP) they provide a taxonomy of tasks and review the maturity of individual research streams . |
| Outcome: | The findings summarize the literature and highlight directions for future work. |
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data. |
| Approach: | They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks. |
| Outcome: | The proposed language model generalizes well across knowledge-grounded dialogue tasks. |