Papers by Rongzhong Lian
Proactive Human-Machine Conversation with Explicit Conversation Goal (P19-1)
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| Challenge: | Typical human-machine conversation systems only use utterances and responses as training data, which results in uninformative and inappropriate responses. |
| Approach: | They propose a dataset where one acts as a conversation leader and the other as 'follower' they establish baseline results on a 270K utterances and 30k dialogues dataset using state-of-the-art models. |
| Outcome: | The proposed model can generate diverse multi-turn conversations using knowledge from a new dataset . |
Dialogue Language Model with Large-Scale Persona Data Engineering (2025.naacl-industry)
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| Challenge: | Existing persona-consistent dialogue models lack robustness due to limited scale and diversity of datasets. |
| Approach: | They propose an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialog dataset to enhance persona consistency. |
| Outcome: | The proposed model generates vast persona dialogue datasets and addresses invalid persona bias. |
Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment (P19-1)
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| Challenge: | Existing approaches to generate informative responses based on external knowledge are limited to singleround settings. |
| Approach: | They propose a framework for multi-turn conversations with two dialogue agents . they propose to evaluate dialogues on informativeness and coherence . |
| Outcome: | The proposed framework outperforms state-of-the-art approaches significantly on the publicly available dataset. |