Papers by Weichao Wang
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment (2023.findings-emnlp)
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Boyang Xue, Weichao Wang, Hongru Wang, Fei Mi, Rui Wang, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Experimental results show that pretrained language models generate inconsistent factual knowledge in many conversational tasks. |
| Approach: | They propose a method which explicitly introduces extended feedforward networks (FFNs) in Transformers to enhance factual knowledge expressions given the specific patterns of knowledge-grounded dialogue inputs. |
| Outcome: | The proposed methods improve the factual expression capability of feedforward networks (FFNs) in knowledge-grounded dialogue systems by knowledge enhancement and alignment respectively. |
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning (D18-1)
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| Challenge: | Existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. |
| Approach: | They propose to use user-attention-based Convolutional Neural Networks to capture individuality and opinion bias in microblog posts and a novel adversarial cross-lingual learning framework to enrich the user post representation. |
| Outcome: | The proposed method outperforms state-of-the-art baseline algorithms with large margins on English and Chinese microblog datasets. |
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues (2023.findings-emnlp)
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Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong
| Challenge: | Existing knowledge-grounded dialogue systems focus on a single knowledge source or ignore the dependency between multiple knowledge sources. |
| Approach: | They propose a framework that integrates multiple knowledge sources and dependencies between them. |
| Outcome: | The proposed framework can produce persona-consistent and knowledge-enhanced responses on a knowledge-grounded dialogue dataset. |
Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder (2022.findings-emnlp)
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| Challenge: | Existing efforts to identify and avoid CDM to facilitate dialogue learning failed to solve the problem. |
| Approach: | They propose a Sentence Semantic Segmentation guided Conditional Variational Auto-Encoder which can model and take advantage of the CDM data. |
| Outcome: | The proposed method can model and take advantages of the CDM data. |
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation (2025.findings-naacl)
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| Challenge: | Radiology report generation has shown great potential in assisting radiologists . generative medical Vision Large Language Models (VLLMs) are prone to hallucinations and can produce inaccurate diagnostic information. |
| Approach: | They propose a framework that provides both report-level and sentence-level uncertainties. |
| Outcome: | The proposed method improves factuality scores by 10% by rejecting 20% of reports on the MIMIC-CXR dataset. |
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval (2024.lrec-main)
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| Challenge: | Existing methods for retrieving information from a large corpus of data are sub-optimal and low efficiency. |
| Approach: | They propose a multi-task framework that functions as a universal retriever for three dominant retrieval tasks during the conversation. |
| Outcome: | The proposed framework can perform persona selection, knowledge selection, and response selection tasks simultaneously. |
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation (D19-1)
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| Challenge: | Existing methods for question generation suffer from dullness and deviation problem, which can lead to deviated or dull questions. |
| Approach: | They propose two methods to enhance semantic coherence between question and answer by using a coherent score and adversarial training to explicitly control question generation. |
| Outcome: | The proposed methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions. |