Search Results for author: Hongshen Chen

Found 29 papers, 9 papers with code

Knowledge Diffusion for Neural Dialogue Generation

1 code implementation ACL 2018 Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, Dawei Yin

Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats.

Dialogue Generation Question Answering +1

Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation

2 code implementations31 Aug 2018 Xisen Jin, Wenqiang Lei, Zhaochun Ren, Hongshen Chen, Shangsong Liang, Yihong Zhao, Dawei Yin

However, the \emph{expensive nature of state labeling} and the \emph{weak interpretability} make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.

Dialogue Generation Dialogue State Tracking

EmpDG: Multiresolution Interactive Empathetic Dialogue Generation

1 code implementation20 Nov 2019 Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, Zhumin Chen

In response to this problem, we propose a multi-resolution adversarial model -- EmpDG, to generate more empathetic responses.

Dialogue Generation

Adaptive Parameterization for Neural Dialogue Generation

1 code implementation IJCNLP 2019 Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao, Dawei Yin

For each conversation, the model generates parameters of the encoder-decoder by referring to the input context.

Dialogue Generation

Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight

no code implementations ACL 2020 Hengyi Cai, Hongshen Chen, Yonghao Song, Cheng Zhang, Xiaofang Zhao, Dawei Yin

In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.

Dialogue Generation

Collaborative Group Learning

no code implementations16 Sep 2020 Shaoxiong Feng, Hongshen Chen, Xuancheng Ren, Zhuoye Ding, Kan Li, Xu sun

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima.

Computational Efficiency Inductive Bias +1

Modeling Topical Relevance for Multi-Turn Dialogue Generation

no code implementations27 Sep 2020 Hainan Zhang, Yanyan Lan, Liang Pang, Hongshen Chen, Zhuoye Ding, Dawei Yin

Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly.

Dialogue Generation Sentence

Regularizing Dialogue Generation by Imitating Implicit Scenarios

no code implementations EMNLP 2020 Shaoxiong Feng, Xuancheng Ren, Hongshen Chen, Bin Sun, Kan Li, Xu sun

Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario.

Dialogue Generation Imitation Learning

EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation

1 code implementation COLING 2020 Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, Zhumin Chen

In response to this problem, we propose a multi-resolution adversarial model {--} EmpDG, to generate more empathetic responses.

Dialogue Generation

User-Inspired Posterior Network for Recommendation Reason Generation

no code implementations16 Feb 2021 Haolan Zhan, Hainan Zhang, Hongshen Chen, Lei Shen, Yanyan Lan, Zhuoye Ding, Dawei Yin

A simple and effective way is to extract keywords directly from the knowledge-base of products, i. e., attributes or title, as the recommendation reason.

Question Answering

Probing Product Description Generation via Posterior Distillation

no code implementations2 Mar 2021 Haolan Zhan, Hainan Zhang, Hongshen Chen, Lei Shen, Zhuoye Ding, Yongjun Bao, Weipeng Yan, Yanyan Lan

To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews.

Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition

no code implementations NAACL 2021 Haolan Zhan, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Yongjun Bao, Yanyan Lan

In particular, a sequential knowledge transition model equipped with a pre-trained knowledge-aware response generator (SKT-KG) formulates the high-level knowledge transition and fully utilizes the limited knowledge data.

Response Generation

Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization

1 code implementation Findings (EMNLP) 2021 Junpeng Liu, Yanyan Zou, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Caixia Yuan, Xiaojie Wang

To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task.

Abstractive Dialogue Summarization Contrastive Learning +3

FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning

no code implementations Findings (EMNLP) 2021 Xu Wang, Hainan Zhang, Shuai Zhao, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Bo Cheng, Yanyan Lan

Furthermore, the consistency signals between each candidate and the speaker's own history are considered to drive a model to prefer a candidate that is logically consistent with the speaker's history logic.

Reading Comprehension

Adaptive Bridge between Training and Inference for Dialogue

no code implementations22 Oct 2021 Haoran Xu, Hainan Zhang, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Yanyan Lan

Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario.

Dialogue Generation NMT +1

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

no code implementations7 Jun 2023 Gangyi Zhang, Chongming Gao, Wenqiang Lei, Xiaojie Guo, Shijun Li, Hongshen Chen, Zhuozhi Ding, Sulong Xu, Lingfei Wu

In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL).

Decision Making Recommendation Systems

Answering Ambiguous Questions via Iterative Prompting

1 code implementation8 Jul 2023 Weiwei Sun, Hengyi Cai, Hongshen Chen, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Zhaochun Ren

To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity.

Open-Domain Question Answering valid

Adaptive Bridge between Training and Inference for Dialogue Generation

no code implementations EMNLP 2021 Haoran Xu, Hainan Zhang, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Yanyan Lan

Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario. In real human dialogue, there are many appropriate responses for the same context, not only with different expressions, but also with different topics.

Dialogue Generation NMT +1

Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation

no code implementations ECNLP (ACL) 2022 Zeming Wang, Yanyan Zou, Yuejian Fang, Hongshen Chen, Mian Ma, Zhuoye Ding, Bo Long

As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms. The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase.

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