Search Results for author: Rongzhong Lian

Found 9 papers, 7 papers with code

Proactive Human-Machine Conversation with Explicit Conversation Goal

1 code implementation ACL 2019 Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, Haifeng Wang

Konv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.

DAL: Dual Adversarial Learning for Dialogue Generation

no code implementations WS 2019 Shaobo Cui, Rongzhong Lian, Di Jiang, Yuanfeng Song, Siqi Bao, Yong Jiang

DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses.

Dialogue Generation

Proactive Human-Machine Conversation with Explicit Conversation Goals

2 code implementations13 Jun 2019 Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, Haifeng Wang

DuConv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.

Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment

1 code implementation ACL 2019 Siqi Bao, Huang He, Fan Wang, Rongzhong Lian, Hua Wu

In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other.

Learning to Select Knowledge for Response Generation in Dialog Systems

1 code implementation13 Feb 2019 Rongzhong Lian, Min Xie, Fan Wang, Jinhua Peng, Hua Wu

Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process.

Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

1 code implementation11 Aug 2018 Di Jiang, Yuanfeng Song, Rongzhong Lian, Siqi Bao, Jinhua Peng, Huang He, Hua Wu

In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models.

Topic Models

Latent Topic Embedding

no code implementations COLING 2016 Di Jiang, Lei Shi, Rongzhong Lian, Hua Wu

Topic modeling and word embedding are two important techniques for deriving latent semantics from data.

Topic Models Word Embeddings

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