Zhonghai Wu


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Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model
Sixing Wu | Ying Li | Ping Xue | Dawei Zhang | Zhonghai Wu
Proceedings of the 29th International Conference on Computational Linguistics

In knowledge-grounded dialogue generation, pre-trained language models (PLMs) can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding. Unlike adopting the plain text knowledge, it is thorny to leverage the structural commonsense knowledge when using PLMs because most PLMs can only operate plain texts. Thus, linearizing commonsense knowledge facts into plan text is a compulsory trick. However, a dialogue is always aligned to a lot of retrieved fact candidates; as a result, the linearized text is always lengthy and then significantly increases the burden of using PLMs. To address this issue, we propose a novel two-stage framework SAKDP. In the first pre-screening stage, we use a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact. Thus, facts can be clustered into three sections of different priorities. As priority decreases, the relevance decreases, and the number of included facts increases. In the next dialogue generation stage, we use section-aware strategies to encode the linearized knowledge. The powerful but expensive PLM is only used for a few facts in the higher priority sections, reaching the performance-efficiency balance. Both the automatic and human evaluation demonstrate the superior performance of this work.

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KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding
Sixing Wu | Ying Li | Dawei Zhang | Zhonghai Wu
Findings of the Association for Computational Linguistics: ACL 2022

Knowledge-enhanced methods have bridged the gap between human beings and machines in generating dialogue responses. However, most previous works solely seek knowledge from a single source, and thus they often fail to obtain available knowledge because of the insufficient coverage of a single knowledge source. To this end, infusing knowledge from multiple sources becomes a trend. This paper proposes a novel approach Knowledge Source Aware Multi-Head Decoding, KSAM, to infuse multi-source knowledge into dialogue generation more efficiently. Rather than following the traditional single decoder paradigm, KSAM uses multiple independent source-aware decoder heads to alleviate three challenging problems in infusing multi-source knowledge, namely, the diversity among different knowledge sources, the indefinite knowledge alignment issue, and the insufficient flexibility/scalability in knowledge usage. Experiments on a Chinese multi-source knowledge-aligned dataset demonstrate the superior performance of KSAM against various competitive approaches.


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More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge
Sixing Wu | Ying Li | Minghui Wang | Dawei Zhang | Yang Zhou | Zhonghai Wu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage. Thus, they often degenerate into traditional methods because not all dialogues can be linked with knowledge entries. This paper proposes a novel dialogue generation model, MSKE-Dialog, to solve this issue with three unique advantages: (1) Rather than only one, MSKE-Dialog can simultaneously leverage multiple heterogeneous knowledge sources (it includes but is not limited to commonsense knowledge facts, text knowledge, infobox knowledge) to improve the knowledge coverage; (2) To avoid the topic conflict among the context and different knowledge sources, we propose a Multi-Reference Selection to better select context/knowledge; (3) We propose a Multi-Reference Generation to generate informative responses by referring to multiple generation references at the same time. Extensive evaluations on a Chinese dataset show the superior performance of this work against various state-of-the-art approaches. To our best knowledge, this work is the first to use the multi-source heterogeneous knowledge in the open-domain knowledge-enhanced dialogue generation.


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Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness
Sixing Wu | Ying Li | Dawei Zhang | Yang Zhou | Zhonghai Wu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Generative dialogue systems tend to produce generic responses, which often leads to boring conversations. For alleviating this issue, Recent studies proposed to retrieve and introduce knowledge facts from knowledge graphs. While this paradigm works to a certain extent, it usually retrieves knowledge facts only based on the entity word itself, without considering the specific dialogue context. Thus, the introduction of the context-irrelevant knowledge facts can impact the quality of generations. To this end, this paper proposes a novel commonsense knowledge-aware dialogue generation model, ConKADI. We design a Felicitous Fact mechanism to help the model focus on the knowledge facts that are highly relevant to the context; furthermore, two techniques, Context-Knowledge Fusion and Flexible Mode Fusion are proposed to facilitate the integration of the knowledge in the ConKADI. We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation. Extensive evaluations over both an open-released English dataset and our Chinese dataset demonstrate that our approach ConKADI outperforms the state-of-the-art approach CCM, in most experiments.

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Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues
Sixing Wu | Ying Li | Dawei Zhang | Zhonghai Wu
Findings of the Association for Computational Linguistics: EMNLP 2020

Incorporating commonsense knowledge can alleviate the issue of generating generic responses in open-domain generative dialogue systems. However, selecting knowledge facts for the dialogue context is still a challenge. The widely used approach Entity Name Matching always retrieves irrelevant facts from the view of local entity words. This paper proposes a novel knowledge selection approach, Prototype-KR, and a knowledge-aware generative model, Prototype-KRG. Given a query, our approach first retrieves a set of prototype dialogues that are relevant to the query. We find knowledge facts used in prototype dialogues usually are highly relevant to the current query; thus, Prototype-KR ranks such knowledge facts based on the semantic similarity and then selects the most appropriate facts. Subsequently, Prototype-KRG can generate an informative response using the selected knowledge facts. Experiments demonstrate that our approach has achieved notable improvements on the most metrics, compared to generative baselines. Meanwhile, compared to IR(Retrieval)-based baselines, responses generated by our approach are more relevant to the context and have comparable informativeness.


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Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
Yao Zhu | Hongzhi Liu | Zhonghai Wu | Yang Song | Tao Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging. Most existing KG completion methods only consider the direct relation between nodes and ignore the relation paths which contain useful information for link prediction. Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning. In addition, these path-based models always ignore nonlinear contributions of path features for link prediction. To solve these problems, we propose a novel KG completion method named OPTransE. Instead of embedding both entities of a relation into the same latent space as in previous methods, we project the head entity and the tail entity of each relation into different spaces to guarantee the order of relations in the path. Meanwhile, we adopt a pooling strategy to extract nonlinear and complex features of different paths to further improve the performance of link prediction. Experimental results on two benchmark datasets show that the proposed model OPTransE performs better than state-of-the-art methods.


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HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation
Sixing Wu | Dawei Zhang | Ying Li | Xing Xie | Zhonghai Wu
Proceedings of the 27th International Conference on Computational Linguistics

Recent years have witnessed a surge of interest on response generation for neural conversation systems. Most existing models are implemented by following the Encoder-Decoder framework and operate sentences of conversations at word-level. The word-level model is suffering from the Unknown Words Issue and the Preference Issue, which seriously impact the quality of generated responses, for example, generated responses may become irrelevant or too general (i.e. safe responses). To address these issues, this paper proposes a hybrid-level Encoder-Decoder model (HL-EncDec), which not only utilizes the word-level features but also character-level features. We conduct several experiments to evaluate HL-EncDec on a Chinese corpus, experimental results show our model significantly outperforms other non-word-level models in automatic metrics and human annotations and is able to generate more informative responses. We also conduct experiments with a small-scale English dataset to show the generalization ability.