Papers by Sixing Wu
Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Entity name matching always retrieves irrelevant facts from the view of local entity words. |
| Approach: | They propose a knowledge selection approach and a generative model that integrates commonsense knowledge into the dialogue response generation by integrating commonsensical knowledge into a query. |
| Outcome: | The proposed approach improves on the most metrics and comparable to baselines. |
MOBA-E2C: Generating MOBA Game Commentaries via Capturing Highlight Events from the Meta-Data (2022.findings-emnlp)
Copied to clipboard
| Challenge: | e-sports game competitions lack commentators because of the shortage of professional human commentators. |
| Approach: | They propose a data-driven MOBA commentary generation framework for MOBA games . they use a rule-based generator and a generative GPT generator to generate commentaries . |
| Outcome: | The proposed model generates commentaries based on the game meta-data and a rule-based generator and generative GPT generator. |
Exploring the Effectiveness of Multi-Lingual Commonsense Knowledge-Aware Open-Domain Dialogue Response Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that commonsense knowledge-aware models can improve informativeness while reducing the hallucination issue. |
| Approach: | They propose a task to use commonsense knowledge in other languages to enhance the current dialogue generation by using commonsensical knowledge in different languages. |
| Outcome: | The proposed model improves the current dialogue generation while reducing the hallucination issue. |
Adversarial Attack against Cross-lingual Knowledge Graph Alignment (2021.emnlp-main)
Copied to clipboard
Zeru Zhang, Zijie Zhang, Yang Zhou, Lingfei Wu, Sixing Wu, Xiaoying Han, Dejing Dou, Tianshi Che, Da Yan
| Challenge: | Existing studies on cross-lingual entity alignment under adversarial attacks have not been conducted. |
| Approach: | They propose to use adversarial attack techniques to perturb cross-lingual entity alignment under adversarials. |
| Outcome: | The proposed model hides the attacked entities in dense regions in two KGs, and reduces the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness. |
Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model (2022.coling-1)
Copied to clipboard
| Challenge: | Pre-trained language models can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding. |
| Approach: | They propose a two-stage framework to integrate a linearized knowledge into plan text using a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact. |
| Outcome: | The proposed framework improves the performance of pre-trained language models by using section-aware strategies to encode the linearized knowledge. |
KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge-enhanced methods only seek knowledge from a single source, resulting in insufficient coverage of a given knowledge source. |
| Approach: | They propose to use multiple independent decoder heads to infuse multi-source knowledge into dialogue generation more efficiently by incorporating external knowledge into the dialogue generation. |
| Outcome: | The proposed approach overcomes three challenges in infusing multi-source knowledge into dialogue generation more efficiently. |
Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness (2020.acl-main)
Copied to clipboard
| Challenge: | Generative dialogue systems tend to produce generic and boring responses, causing boring conversations . a novel commonsense knowledge-aware dialogue generation model is proposed to solve this problem . |
| Approach: | They propose to retrieve and introduce knowledge facts from knowledge graphs to reduce boring conversations . they use a Felicitous Fact mechanism to help the model focus on context-relevant knowledge facts . |
| Outcome: | The proposed model outperforms the state-of-the-art approach in most experiments. |
LLMs as Collaborator: Demands-Guided Collaborative Retrieval-Augmented Generation for Commonsense Knowledge-Grounded Open-Domain Dialogue Systems (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to capture the unique knowledge demands of LLMs are unsatisfactory because of their overestimation and lack of knowledge. |
| Approach: | They propose a novel approach to capture the unique knowledge demands for each dialogue context using CoT and RAG methods. |
| Outcome: | The proposed model can capture the unique knowledge demands for each dialogue context and bring higher-quality responses. |
More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge-enhanced methods use a single-source homogeneous knowledge base with limited knowledge coverage. |
| Approach: | They propose a multi-source heterogeneous knowledge-enhanced dialogue generation model that leverages multiple knowledge sources to improve knowledge coverage. |
| Outcome: | The proposed model outperforms existing knowledge-enhanced models on a Chinese dataset and shows that it can leverage multiple heterogeneous knowledge sources to improve knowledge coverage. |
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation (C18-1)
Copied to clipboard
| Challenge: | Existing models for conversation systems operate sentences at word-level . word-based models suffer from Unknown Words Issue and Preference Issue . |
| Approach: | They propose a hybrid-level Encoder-Decoder model which utilizes word-level features and character-level ones. |
| Outcome: | The proposed model outperforms non-word-level models in automatic metrics and human annotations on a Chinese corpus. |