PK-Chat: Pointer Network Guided Knowledge Driven Generative
Dialogue Model
Cheng Deng1 , Bo Tong1 , Jiaxin Ding1 , Luoyi Fu1 , Xinde Cao1 , Xinbing Wang1 , Chenghu Zhou1,2
1 Shanghai Jiao Tong University,

2 Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences

davendw@sjtu.edu.cn
Corresponding authors: Jiaxin Ding and Luoyi Fu

ABSTRACTIn the realm of end-to-end dialogue systems research, leveraging
real-world knowledge to produce natural, fluent, and human-like responses with accurate information is paramount. However, domainspecific conversational dialogue systems often grapple with coherence issues and may inadvertently incorporate incorrect external
information while responding to queries. This challenge stems
from out-of-vocabulary problems and misinformation arising fromthe parameters of the neural network. In this study, we present
PK-Chat, a groundbreaking Pointer network guided Knowledgedriven generative dialogue model. PK-Chat integrates a unified
pre-trained language model with a pointer network for knowledge
graphs. The words generated by PK-Chat within the dialogue arederived from predictions made using word lists and direct information from the external knowledge graph. Furthermore, we employ
PK-Chat to develop a dialogue system tailored for academic contexts, specifically within the field of geosciences. To assess thequality of dialogue systems in academic scenarios, we establish an
academic dialogue benchmark. The source code for our work is
publicly available online 1 .

These knowledge graphs are embedded into vectors within latentsemantic spaces, and these embeddings are utilized to generate
relevant text candidate sets [20]. However, there is a challenge
in maintaining semantic coherence, as the probability of each utterance neighbor candidate is calculated independently, without
considering the relationship between the candidate utterances andthe contextual input [14]. Additionally, existing systems face a hurdle when dealing with unseen or out-of-vocabulary words fromthe knowledge graph. Pretrained models lack position information
for referenced knowledge, making it challenging to identify specific meanings if certain words do not appear or are unlikely toappear in the model’s tokenizer. This limitation can lead to misusing
memorized knowledge in generating responses [2].

CCS CONCEPTS• Computing methodologies → Discourse, dialogue and pragmatics.

KEYWORDS
Dialogue System, Pointer Network, Academic Knowledge Graph,
Natural language Generation
ACM Reference Format:
Cheng Deng1 , Bo Tong1 , Jiaxin Ding1 , Luoyi Fu1 , Xinde Cao1 , Xinbing
Wang1 , Chenghu Zhou1,2 . 2023. PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model. In Proceedings of The 12th International Joint Conference on Knowledge Graphs (IJCKG ’23). ACM, Tokyo,
Japan, 5 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

1

INTRODUCTION

Developing dialogue systems using advanced language models like
PLATO and GPT [1, 15] has become a prominent research direction.
Fine-tuning on such models enables the generation of human-likeconversational responses. However, existing generative dialoguesystems often produce general-purpose responses, leading to a lackof domain expertise and semantic coherence in the replies [22].
To address this issue, researchers have integrated knowledge
graphs such as Freebase [8] and Yago [6] into dialogue systems [24].
1 https://github.com/iiot-tbb/Dialogue_DDE

Figure 1: Overview of PK-Chat with GAKG [4].
Faced with the challenges of knowledge-driven generative dialogue models, we incorporate pointer networks [18, 21] to transfer
information from the original input text to the output text while preserving detailed information. Consider text summarization, wherewords from the original corpus might not have been encounteredduring training. Models lacking a pointer network struggle to restore the original details, often resulting in generated summaries
containing inaccuracies. In contrast, a pointer network enables therestoration of details by retaining original text information when
encountering previously unseen words [18, 19].
In this study, we develop a knowledge-driven generative dialogue model guided by a pointer network. We train the model using
GAKG [4] to facilitate natural and fluent knowledge-informed dialogues with users, explicitly focusing on geology-related knowledge.
The system overview is depicted in Figure 1. The contributions of
this paper can be summarized as follows:
1 This research paper introduces PK-Chat, a novel dialogue generation model that combines a pre-trained language model with apointer generation network through a flexible self-attentive mechanism. By leveraging this advanced approach, PK-Chat demonstrates superior performance compared to established baselines
across various benchmarks.

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

2 PK-Chat innovatively adopts the pointer network with unified
pre-trained language models to guide domain-specific conversation generation, a key contribution towards advancing the stateof-the-art in this domain.
3 Alongside PK-Chat, we construct GA-Dialogue, the first academic
dialogue dataset with words sourced from the GAKG. The availability of this dataset represents a significant advancement, as it
can be used to train other dialogue generation models, further
contributing to the development of this promising field.

2

METHODOLOGY

In this section, we introduce PK-Chat, a novel dialogue generation
model that leverages a unified pre-trained language model andpointer generation network through a self-attentive mechanism. PKChat is designed to generate fluent and natural text that draws on
specific domain knowledge based on academic knowledge graphs.
To achieve this, PK-Chat consists of a dialogue generation model,
knowledge graph retrieval, and keyword extraction subsystems thatwork collaboratively to produce intelligent and context-sensitive
responses to user inquiries.
First, when a user inputs an utterance, the model determines
whether the input is relevant to the current referenced knowledge
range. Suppose the current user utterance conflicts with the referenced knowledge, then the knowledge extraction algorithm will be
activated to extract the text’s keyword or entity information. The
graph query statement is applied to query the specific connected
triples information with the keyword of the entity in the knowledge
graph, and all directly connected edges and tail entities with thenode will be recalled as the knowledge input part of the model,
which is combined with the utterance of the user to generate a
response.

2.1

Dialogue Generation

In order to generate reasonable dialogue responses, the generativemodel should fully understand the above dialogue history information and background knowledge so that the responses are accurate
and consistent enough.
Since the unification of bidirectional, unidirectional, andsequence-to-sequence objective functions enables us to straightforwardly fine-tune the pre-trained language UniLM[5] for both
NLU and NLG tasks and dialogue tasks can benefit from it, we
adopted a parameter sharing self-attention mechanism transformers like UniLM-PLATO based conversational language model as the
backbone of the PK-Chat, which train from social media corpus.
We fine-tune it with the conversational corpus from the data in
GAKG illustrated in section 3. Besides, the loss functions for ourtask include the negative log-likelihood function used in the dialogue response generation and the pointer generation part, thebag-of-words model loss function used when predicting the words
that should be in the responses, and the cross-entropy loss functionused for topic switching.
Response Generation. For a given contextual information 𝑐 anda selected hidden variable 𝑧, the reply is given as 𝑃 (𝑟 | 𝑐, 𝑧) based on
this. Where 𝑧 ∈ [1, 𝐾], each specific z value corresponds to a potential semantic behavior, and the identification of the corresponding

Cheng Deng, et al.

hidden variable can be accomplished by argmax(𝑃 (𝑧 | 𝑟, 𝑐)) for the
given contextual information and response content.
In PK-Chat, the response generation consists mainly of discrete
hidden variables, content, and knowledge information. Moreover,take the maximum likelihood estimation function as the loss function like Equation 1.
L𝑁 𝐿𝐿 = −E𝑧∼𝑝 (z|𝑐,𝑘,𝑟 ) log 𝑝 (𝑟 | 𝑐, 𝑘, 𝑧)= −E𝑧∼𝑝 (z|𝑐,𝑘,𝑟 )𝑇∑︁log 𝑝 (𝑟𝑡 | 𝑐, 𝑘, 𝑧, 𝑟 <𝑡 ) ,

(1)

𝑡 =1

where 𝑧 is the discrete hidden variable obtained from (𝑐, 𝑘, 𝑟 ) andbased on the probability 𝑝 (z | 𝑐, 𝑘, 𝑟 ) are sampled. The hidden variable identification task obtains the distribution of the posterior
probabilities of the hidden variables. 𝑐 is the conversation information above, and 𝑘 is the external knowledge information. And
𝑝 (z | 𝑐, 𝑘, 𝑟 ) is a softmax activate function as Equation 2,
𝑝 (z | 𝑐, 𝑘, 𝑟 ) = softmax 𝑊1ℎ [𝑀 ] + 𝑏 1 ∈ R𝐾 ,(2)
where 𝑧 ∈ R𝐾 , ℎ [𝑀 ] ∈ R𝐾 are the status tokens for the last layer of
special status marker location. 𝑊1 ∈ R𝐾 ×𝐷 and 𝑏 1 ∈ R𝐾 denotes
the trainable parameters.
For each word 𝑤 in the response generation, the prediction ismade by the word corresponding word list, context, and knowledgeembedded information. PK-Chat adopts the pointer network, making the references to external knowledge more accurate. The probability is calculated as Equation 3,
𝑃 (𝑤) = 𝜆𝑔𝑒𝑛 𝑃 vocab (𝑤) + 1 − 𝜆𝑔𝑒𝑛

 ∑︁

𝑎𝑖𝑡 ,

(3)

𝑖:𝑤𝑖 =𝑤

where 𝜆𝑔𝑒𝑛 = sigmoid (𝑊2ℎ𝐷 + 𝑏 2 ), 𝑊2 and 𝑏 2 are trainable parameters, ℎ𝐷 is a hidden state of the intermediate generation result,and 𝑎 is denoted as the prediction of the knowledge embedding
location among the context and the pointer.
In addition to the negative log-likelihood estimation of the direct
task goal of generating dialogue responses, the loss function of
the bag-of-words model is added to the model training process
to achieve the learning of the hidden variable 𝑧 by predicting thewords in the bag of words, specifically by predicting the words thatshould be in the responses through the hidden state of the last layerof 𝑧. Such a multi-task model can also accelerate the convergence
speed of the model training. And the loss function is Equation 4.
L𝐵𝑂𝑊 = −E𝑧∼𝑝 (z|𝑐,𝑘,𝑟 )= −E𝑧∼𝑝 (z|𝑐,𝑘,𝑟 )

𝑇
∑︁
𝑡 =1
𝑇
∑︁
𝑡 =1

log 𝑝 (𝑟𝑡 | 𝑐, 𝑘, 𝑧)
(4)
log Í

𝑒 𝑓𝑟𝑡
,
𝑓
𝑣 ∈𝑉 𝑒 𝑣

where 𝑉 represents the size of the word list, 𝑓 is the softmax function 𝑓 = softmax (𝑊3ℎ𝑧 + 𝑏 3 ) ∈ R |𝑉 | that predicts the words inthe target generation, and 𝑓𝑟𝑡 represents the probability value ofthe words generated at each moment. This prediction does notcorrelate to the order of each word but to the intention of making
the hidden state variables capture more global information throughthis learning approach.

PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model

Topic Switch. In practice, we should select the correct external
knowledge under the appropriate topic and judge whether we needto switch knowledge by comparing the current user’s words andthe context. Therefore, the judgment of topic switching is necessary,and we can select different knowledge at the appropriate moment. In
this model, topic switching is a binary classification task to classify
whether the current knowledge matches the current user utterance.
If it does, the current topic knowledge is maintained and keeps
chatting on the current topic. When the current knowledge does notmatch the question the user asks, the keyword extraction module
is triggered, and the corresponding entity and edge informationof the extracted keyword are queried in the knowledge graph. We
choose the cross-entropy loss function as Equation 5.

L𝑇 𝑆 = − log 𝑝 𝑙 true = 1 | 𝑘, 𝑐, 𝑟 + −log 𝑝 (𝑙 true = 0 | 𝑘, 𝑐, 𝑟 − ) . (5)Give the knowledge during the dialogue with 𝑙 true = 1, randomly
sample the knowledge in the other topic, and label it as 𝑙 true = 0.
Overall, the loss function of the whole model is:
L = L𝑁 𝐿𝐿 + L𝐵𝑂𝑊 + L𝑇 𝑆 ,(6)where L𝑁 𝐿𝐿 acts directly on the generation purpose, L𝐵𝑂𝑊 actson the hidden state learning and assists in the generation task.
The L𝑇 𝐽 is used for topic classification, so the whole model uses amulti-task learning method.

2.2

Keyword Extraction

When the user’s utterances mention entities that are in the knowledge graph, the critical information will be extracted via rule-based
keyword extraction method, TF-IDF [17], TextRank [13] and BiLSTM+CRF [7] NER methods to extract the current entity during
the communication with the user.
• We use a rule-based method by constructing regular expressions
like “(what|which|where)(is|are)(the)[a-z]{0,5}?” to match the questioning phrase, which can quickly locate the corresponding keyword.
• We use TF-IDF and TextRank to obtain the most important words
by multiplying the word frequency of a word and its inverse
document frequency to indicate the importance of a word.
• We also use the BiLSTM-CRF model that defines the knowledge
information extraction of user conversations as a sequence annotation task for keyword extraction.
Regarding the keyword extraction training data, TF-IDF and
TextRank are unsupervised methods that do not require constructing labeled data for training. Hence, the dataset construction for
information extraction mainly enables the BILSTM+CRF model toperform well on this keyword extraction task. According to thecharacteristics of the dialogue data in this paper, the entity information in the dialogue refers to the entities in the knowledge graph,so the annotation task in this part does not need much manual
annotation. We only need to search and locate the entities in the
dialogue and do the automatic annotation.

2.3

Retrieve over Knowledge Graph

In order to ensure the efficiency of the knowledge retrieval, wechoose a reasonable storage method for the external knowledge

IJCKG ’23, December 08–09, 2023, Tokyo, Japangraph. In this paper, we choose GAKG, an academic knowledge
graph in geoscience, to deploy an academic dialogue system.
The GAKG is a collection of papers’ illustrations, text, and bibliometric data. It is currently the largest and most comprehensive
geoscience academic knowledge graph, consisting of more than 120
million triples with 11 kinds of concepts connected by 19 relations,stored in RDF format. We download the full copy of GAKG and store
it in the graph database (Neo4J). After that, we built a GA-Dialogue
dataset to train an academic chatbot. First, we randomly sampled
all the information about the connected edges and tail entities of asingle head entity on the knowledge graph of GAKG, constructed a
specific dialogue scenario based on the sampled information, andstarted a specific dialogue around the information of the entity, i.e.,
we quoted the information of the entity in the dialogue to reply. In
order to improve the quality of the dialogue dataset, we invited 20
geographers who understand the details of GAKG to participate
in the construction and let them retain the label format of the entity. Five hundred fifty dialogue scenarios and 3,615 dialogues wereconstructed in GA-Dialogue. The average number of utterances ofusers per scenario is 6.7.
However, the number of dialogue datasets is not large enough,so we increase the data by cleaning and constructing the public
dataset. We used the Baidu DuConv [23] and Baidu DuRecDial [12]dialogue datasets as external datasets to introduce. For the DuConv
dataset, there are 29,858 conversations in the scenes, with an average of 9 rounds of conversation per scene. In order to unify the datain this dialogue dataset with the dialogue data in our GAKG, weconstruct two types of knowledge in the conversation dataset: conversation goals and knowledge. We integrate the conversation goals
and knowledge aggregated in the conversation dataset into theknowledge as the unified external knowledge. For the DuRecDial
dataset, there are 10,200 conversations in the scenes, with an average of 15 rounds of conversation per scene. There are three typesof valuable knowledge in the conversation dataset: conversation
goals, knowledge, and user profile. We integrate the conversation
goals, knowledge, and user profile aggregated in the conversation
dataset as the knowledge. In this way, the data format is alignedwith the conversation format of GAKG.
We sampled a few data, and finally, GA-Dialogue has 1,000 dialogue scenarios and 8219 dialogue rounds, with an average of 8.21
dialogue rounds per scenario.

3

EXPERIMENTIn this section, we evaluate the automatic evaluation results andthe human evaluation results of the model of the dialogue system.
This section details the models’ benchmarks and evaluation metrics
in experimental setup and evaluation results.

3.1

Experimental Setup

In this subsection, we briefly introduce the benchmarks, baselines,
and metrics we selected to experiment with our model.
Benchmarks. We choose Persona-Chat [25] and DailyDialog [10]as the general benchmark, and we build an Academic KnowledgeGraph-based dialogue benchmark GA-Dialogue.
• Persona-Chat is a dataset of knowledge-based conversations on
persona profiles (background knowledge).

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

Cheng Deng, et al.

Automatic Evaluation

Human Evaluation

DatasetModel

BLEU-1/2

Distinct-1/2

Knowledge R/P/F1

Readability

Relevance

Consistency

Informativeness

Naturalness

GA-Dialogue (part1)

PLATO (Unidirect)
PLATO
PK-Chat (Ours)

0.054/0.042
0.415/0.354
0.636/0.532

0.099/0.270
0.165/0.361
0.139/0.366

0.002/0.011/0.003
0.099/0.218/0.124
0.100/0.228/0.128

0.60
2.67
2.73

0.50
2.10
2.26

0.502
2.23
2.40

0.40
2.37
2.43

0.37
2.20
1.90

GA-Dialogue (part2)

PLATO (Unidirect)
PLATO
PK-Chat (Ours)

0.106/0.086
0.342/0.268
0.496/0.383

0.050/0.137
0.105/0.322
0.065/0.237

0.002/0.048/0.003
0.022/0.246/0.040
0.044/0.273/0.074

0.00
2.93
2.80

0.03
2.53
2.43

0.03
2.50
2.57

0.03
2.63
2.80

0.03
2.33
2.60

Persona-Chat

PLATO (Unidirect)
PLATO
PK-Chat (Ours)

0.231/0.178
0.257/0.199

0.003/0.010
0.014/0.053
0.015/0.062

0.018/0.084/0.028
0.028/0.138/0.044
0.026/0.131/0.042

0.30
2.83
2.57

0.24
2.17
1.96

0.20
2.30
2.17

0.27
1.97
2.03

0.13
2.33
2.34

DailyDialog

PLATO (Unidirect)
PLATO
PK-Chat (Ours)

0153/0.117
0.388/0.304
0.416/0.329

0.042/0.153
0.055/0.303
0.049/0.282

-

2.71
2.57
2.77

1.97
2.33
2.71

1.67
2.10
2.53

1.33
2.07
2.63

1.27
1.90
2.41

Table 1: Comparison with baselines.
• DailyDialog is a chitchat dataset containing high-quality humanconversations about everyday life.
• GA-Dialogue (Part 1 & 2), we divide the GA-Dialogue test dataset
into two parts: the first part (part1) of the set contains dialogue
data on the knowledge graph of GAKG, and the second part
(part2) of the dialogue evaluation comes from the evaluation of
the dialogue data introduced by external dataset. The datasets
are available at Github Repo.2
Baselines. We choose the PLATO [1] and PLATO (Unidirect) as
the baselines since PLATO achieves the sota results for the known
model size of the same scale and PLATO (Unidirectional) is chosen
as the baseline model to analyze the effect of the unidirectional
attention mechanism on the final generation of the model, it isconsistent with the GPT [16] series in the model self-attention
structure.
Metrics. Unlike task-oriented dialogue systems, open-domain
dialogue systems are complicated to evaluate the performance ofdialogue systems through a specific metric due to the flexibilityof the dialogue. In general, the open-domain dialogue systems
are measured through objective and subjective evaluations, andthe automatic and human evaluation methods used in this paper
compare each model.
We choose BLEU [3] (bilingual evaluation understudy), Distinct [9] and Knowledge [1] as the Automatic Evaluation Metric,
and greater the metrics are the better the models perform.
• BLEU is used to evaluate the generation task determined by calculating the overlap between the generated responses and the𝑛-gram of the tags. In this paper, we set 𝑛 as 1 and 2.
• Distinct is set up to measure diversity rubric for evaluating generated sentences by counting the ratio of unique 𝑛-gram of thewords. In this paper, we set 𝑛 as 1 and 2.
• Knowledge determines whether the cited knowledge is correct
or incorrect.
For the human evaluation method, the human evaluation includes five indicators as described in [11], and we use them as the
Human Evaluation Metrics in this paper: Readability, Relevance,
Consistency, Informativeness, and Naturalness. In each benchmark,500 generated dialogues and their contextual information are randomly selected as evaluation data, and 20 geoscientists are invited
2 https://github.com/davendw49/PK-Chatto analyze the dialogue performance evaluation and score themfrom [0,1,2,3] points in the above five aspects.

3.2

Experimental Result

In GA-Dialogue (part1), the PK-Chat model outperforms the PLATO
model in BLEU, Distinct, and Knowledge metrics, and in GADialogue (part2), it outperforms the PLATO model and the baselinemodel in BLEU metrics and Knowledge metrics. The PK-Chat outperforms the PLATO model in all five dimensions of the humanevaluation metrics, with four of the highest metrics in the firstpart of the evaluation set and three of the highest in the second
part. Thus, both the automatic and human evaluation metrics have
improved.
Similarly, in the Persona-Chat dataset, the PK-Chat model outperforms the baseline models on the automatic measures BLEU
and Distinct but slightly underperforms the PLATO model on the
Knowledge measure. The PK-Chat model outperforms the PLATO
model in terms of the human evaluation metrics, and our proposed
method does not have any gain on the final dialogue generationin this part of the dialogue dataset since the dataset references theknowledge of the user task portrait of the knowledge part is rarely
directly referenced when answering the questions.
As for DailyDialog, the proposed method in this paper outperforms the baseline model on this dataset for automatic and humanevaluation metrics. For the information whose context is a historical conversation, the model in this paper can enhance the metrics.
By observation on the dataset, compared with the Persona-Chat,the conversation content usually revolves around the same topic,
and the coherence between the conversation and context of thechitchat is more robust, so the model can replicate the learning of
the words in the previous question through the pointer network sothat the model will have a good performance effect.

4

CONCLUSION

This paper proposes PK-Chat, a knowledge graph-enhanced model
via a unified pre-trained language model and pointer generation
network to realize academic dialogues, aiming to develop a fluent,natural, and knowledge-informative dialogical interaction withscholars. By combining a unified pre-trained language model and apointer network, the model could accurately refer to the knowledge
mentioned in the KGs. Moreover, we put forward a GA-Dialogue
as a benchmark to evaluate dialogue agents.

PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue ModelIJCKG ’23, December 08–09, 2023, Tokyo, JapanACKNOWLEDGMENT

REFERENCES

We would like to acknowledge supports from National key researchand development plan No. 2022YFB3904204, National Natural Science Foundation of China under Grant (No. 42050105, 62202299,
62020106005, 62061146002, 61960206002), Natural Science Foundation of Shanghai No. 22ZR1429100, Shanghai Pilot Program for
Basic Research - Shanghai Jiao Tong University. The first author
would like to thank Wu Wen Jun Honorary Doctoral Scholarship,
AI Institute, Shanghai Jiao Tong University. Moreover, this work
is also a contribution to the Deep-time Digital Earth (DDE) Big
Science Program.

[1] Siqi Bao, H. He, Fan Wang, and Hua Wu. 2020. PLATO: Pre-trained Dialogue
Generation Model with Discrete Latent Variable. In ACL.
[2] Siqi Bao, H. He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhen Guo,
Zhibin Liu, and Xinchao Xu. 2021. PLATO-2: Towards Building an Open-Domain
Chatbot via Curriculum Learning. In FINDINGS.
[3] Boxing Chen and Colin Cherry. 2014. A Systematic Comparison of Smoothing
Techniques for Sentence-Level BLEU. In WMT@ACL.
[4] Cheng Deng, Yuting Jia, Hui Xu, Chong Zhang, Jingyao Tang, Luoyi Fu, Weinan
Zhang, Haisong Zhang, Xinbing Wang, and Chenghu Zhou. 2021. GAKG: A
Multimodal Geoscience Academic Knowledge Graph. Proceedings of the 30th
ACM International Conference on Information & Knowledge Management (2021).
[5] Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang,
Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified Language
Model Pre-training for Natural Language Understanding and Generation.
arXiv:1905.03197 [cs.CL][6] MS Fabian, Kasneci Gjergji, WEIKUM Gerhard, et al. 2007. Yago: A core ofsemantic knowledge unifying wordnet and wikipedia. In 16th International World
Wide Web Conference, WWW. 697–706.
[7] Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF Models for
Sequence Tagging. ArXiv abs/1508.01991 (2015).
[8] Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas,
Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören
Auer, et al. 2015. Dbpedia–a large-scale, multilingual knowledge base extracted
from wikipedia. Semantic web 6, 2 (2015), 167–195.
[9] Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B. Dolan. 2016.
A Diversity-Promoting Objective Function for Neural Conversation Models. In
NAACL.
[10] Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017.
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. In IJCNLP.
[11] Hongru Liang and Huaqing Li. 2021. Towards Standard Criteria for humanevaluation of Chatbots: A Survey. ArXiv abs/2105.11197 (2021).
[12] Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, and Ting
Liu. 2020. Towards Conversational Recommendation over Multi-Type Dialogs.
In ACL.
[13] Rada Mihalcea and Paul Tarau. 2004. TextRank: Bringing Order into Text. In
EMNLP.
[14] Lisong Qiu, Juntao Li, Wei Bi, Dongyan Zhao, and Rui Yan. 2019. Are Training
Samples Correlated? Learning to Generate Dialogue Responses with Multiple
References. In ACL.
[15] Alec Radford and Karthik Narasimhan. 2018. Improving Language Understandingby Generative Pre-Training. In ArXiv.
[16] Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya
Sutskever. 2019. Language Models are Unsupervised Multitask Learners.
[17] Juan Enrique Ramos. 2003. Using TF-IDF to Determine Word Relevance inDocument Queries.
[18] A. See, Peter J. Liu, and Christopher D. Manning. 2017. Get To The Point:
Summarization with Pointer-Generator Networks. ArXiv abs/1704.04368 (2017).
[19] Fei Sun, Peng Jiang, Hanxiao Sun, Changhua Pei, Wenwu Ou, and Xiaobo Wang.
2018. Multi-Source Pointer Network for Product Title Summarization. Proceedings of the 27th ACM International Conference on Information and Knowledge
Management (2018).
[20] Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin
Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019. ERNIE: Enhanced Representation through Knowledge Integration. ArXiv abs/1904.09223 (2019).
[21] Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer Networks. In
NIPS.
[22] Yanmeng Wang, Ye Wang, Xingyu Lou, Wenge Rong, Zhenghong Hao, andShaojun Wang. 2021. Improving Dialogue Response Generation Via Knowledge
Graph Filter. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speechand Signal Processing (ICASSP) (2021), 7423–7427.
[23] Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong
Lian, and Haifeng Wang. 2019. Proactive Human-Machine Conversation with
Explicit Conversation Goal. In ACL.
[24] Shiquan Yang, Rui Zhang, and Sarah Monazam Erfani. 2020. GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems. In
EMNLP.
[25] Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur D. Szlam, Douwe Kiela, andJason Weston. 2018. Personalizing Dialogue Agents: I have a dog, do you havepets too?. In ACL.