An Entity-Driven Recursive Neural Network Model for Chinese Discourse Coherence Modeling Full Text

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.8, No.2, March 2017
DOI : 10.5121/ijaia.2017.8201 1
AN ENTITY-DRIVEN RECURSIVE NEURAL
NETWORK MODEL FOR CHINESE DISCOURSE
COHERENCE MODELING
Fan Xu, Shujing Du, Maoxi Li and Mingwen Wang
School of Computer Information Engineering, Jiangxi Normal University
Nanchang 330022, China
ABSTRACT
Chinese discourse coherence modeling remains a challenge taskin Natural Language Processing
field.Existing approaches mostlyfocus on the need for feature engineering, whichadoptthe sophisticated
features to capture the logic or syntactic or semantic relationships acrosssentences within a text.In this
paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse coherence evaluation
based on current English discourse coherenceneural network model. Specifically, to overcome the
shortage of identifying the entity(nouns) overlap across sentences in the currentmodel, Our combined
modelsuccessfully investigatesthe entities information into the recursive neural network
freamework.Evaluation results on both sentence ordering and machine translation coherence rating
task show the effectiveness of the proposed model, which significantly outperforms the existing strong
baseline.
KEYWORDS
Entity, Recursive Neural Network, Chinese Discourse, Coherence
1. INTRODUCTION
Discourse Coherence Modeling (DCM) aims to evaluate a degree of coherence among sentences
within a discourse or text. It is considered one of the key problems in Natural Language
Processing (NLP) due to its wide usage in many NLP applications, such as statistical machine
translation[1]
, discourse generation[2][3][4]
, text automation summarization[3][5][6]
, student essay
scoring[7][8][9]
.
In general, a coherent discourse generally has many similar components (lexical overlap or
coreference) across sentences within a text, while incoherent discourse is the other one.
Therefore, the traditional cohesion theory of Centering[10]
driven and entity-based
model[11][12][13][14]
was proposed to capture the syntactic or semantic distribution of discourse
entities (nouns) between two adjacent sentences in a text. Thereafter, many extension works were
presented such as Feng and Hirst[15]
’s multiple ranking model, Lin et al.[16]
’s discourse relation-
based approach, Louis and Nenkova[17]
’s syntactic patterns-based model. However, the potential
issue of the existing traditional coherence models need feature engineering, which is a time-
consuming job.
In order to overcome the limitation of feature engineering issue, modern research tries to use
neural network to extract the syntactic or semantic representation of a sentence automatically. Li
et al.[18]
proposed neural deep model to deal with English discourse coherence evaluation.
However, their discourse coherence model only focuses on the distributed representation for

2
sentences, and did not consider the entity (nouns) distribution across sentences. In fact, the
entities can be overlapped between two adjacent sentences, and are good insight to capture the
coherence between adjacent two sentences as mentioned in traditional entity-based method.
Therefore, we successfully integrate this kind of information into current recursive neural
network framework. Evaluation results on both sentence ordering and machine translation
coherence rating task show the effectiveness of the proposed model, which significantly
outperforms the existing strong baseline.
Therefore, this paper tries to answer the following three questions:
(1) Can the current English discourse coherence models (traditional or neural method) work
for Chinese discourse coherence evaluation task?
(2) Can the traditional entity based model be integrated into current deep model?
(3) Which kind of word embedding works better for Chinese discourse coherence evaluation?
The rest of this paper is organized as follows. Section 2 reviews related work on discourse
coherence modeling. Section 3 introduces the framework of our entity-driven recursive neural
network based Chinese discourse coherence model. Section 4 describes the experiment results
and detailed analysis. Finally, some conclusions are drawn in Section 5.
2. RELATED WORK
In this section, we describe the related work for discourse coherence modeling from traditional
and neural network modes, respectively.
2.1. TRADITIONAL COHERENCE MODEL
The task of DCM was first introduced by Foltz et al.[19]
. They formulated the discourse coherence
as a function of semantic relatedness between two adjacent sentences within a text, and employed
a vector-based representation of lexical meaning to compute the semantic relatedness. Since then,
many supervised approaches to DCM, such as the entity-based model[11][12][13][15]
, discourse
relation-based model[16]
, syntactic patterns-based model[17]
, co reference resolution-based
model[20][21]
, content-based model via Hidden Markov Model (HMM)[3][22]
and cohesion-driven
based model[23]
have been proposed in literature.
To be more specific, Barzilay and Lapata[11][12]
presented an entity-based model to capture the
distribution of discourse entities between two adjacent sentences within a text. As an extensive
work of entity-based approach, Lin et al.[16]
explored the function of discourse relations to revise
the entity and to catch the behavior of discourse relation transfer among sentences. In addition,
Feng and Hirst[15]
showed that multiple ranking instead of pair wise ranking was effective for the
DCM.
Differently, Louis and Nenkova[17]
explored the function of syntactic structure in the DCM.
Besides, Iida et al.[20]
and Elsner et al.[21]
demonstrated the importance of the usage of co
reference resolution. In addition, Barzilay et al.[3]
and Elsner et al.[22]
showed that an Hidden
Markov Model (HMM)-based content model can be used to capture the topic’s transfer from the
first sentence to the end sentence of a text, where topics were formulated as hidden states and
sentences were treated as observations. Still, a potential issue of the HMM model is its domain-
dependent mechanism. Also, Xu et al.[23]
explored the impact of Halliday[24]
’s Theme Structure
Theory (TST) in English discourse coherence modeling. Their model shows the importance of the
theme structure, a cohesion theory of Halliday’s systemic-functional grammar, to DCM, and the
appropriateness of theme and co reference based filtering mechanism.

3
Figure 1: The framework of entity-driven recursive model for Chinese Discourse Coherence Modeling.
2.2. NEURAL COHERENCE MODEL
Recently, Li et al.[18]
presented a neural deep model for English discourse coherence modeling.
They demonstrated the effectiveness of both recurrent and recursive neural network (RNN) model
for English situation.
However, as mentioned in the Section 1, their model did not consider the entity (nouns)
distribution or entity overlap across sentences within a text. In fact, the entity overlap between
two adjacent sentences indicates logical or semantic coherence for para text. Therefore, we
successfully integrate these information into their model.
3. ENTITY-DRIVEN RNN COHERENCE MODEL
In this section, we describe our entity-driven RNN Chinese discourse coherence model.
3.1. FRAMEWORK
Figure 1 shows the entity-driven recursive deep model for Chinese discourse coherence
modeling. Our deep model is based on Li et al.[18]
’s English discourse coherence framework. On
comparison, their model doesn’t intensify the effectiveness of entities across each sentences in a
text. Therefore, we successfully integrate the entities into current recursive neural network model.
3.2. SENTENCE REPRESENTATION
For the word-level representation, each word in a sentence can be represented by using a vector
representation (or word embedding), and are able to capture the semantic meanings through
toolkit, e.g. word2vec1
or Glove2
. More specifically, the word of a sentence can be represented
using a specific vector embedding ew={ew
1
,ew
2
,…,ew
K
},where K denotes the dimension of the word
embedding.
1
http://code.google.com/p/word2vec/
2
http://nlp.stanford.edu/projects/glove/

4
For the sentence-level representation, as shown in Figure 1, the vector representation for the
whole sentence is computed as a representation for each parent node based on its immediate
children recursively in a bottom-up fashion until reaching the root of the tree. Concretely, for a
given parent p in the tree and its two children c1(associated with vector representation hc1) and
c2(associated with vector representation hc2), standard recursive network calculates hpfor p as
follows:
hp=f(WRecursive.[hc1, hc2]+bRecursive) (1)
where [hc1, hc2] refers to the concatenating vector for children vector hc1 and hc2;WRecursive is a k*2K
matrix and bRecursive is the K*1 bias vector; f(.) is tanh function.
3.3. ENTITY-DRIVEN SENTENCE CONVOLUTION
The framework treats a window of sentences as a clique C(sliding windows of L sentences)and
associates each clique with a tag yc that takes the value 1 if coherent, and 0 otherwise. As shown
in Figure 1, each clique C takes as input a (L*K)*1vector hc by concatenating the embedding of
all its contained sentences. The hidden layer takes as input hc and performs the convolution using
a non-linear tanh function. The concatenating output vector for hidden layers, defined as qc, can
therefore be rewritten as:
qc=f(Wsen*(hc*hentity)+bsen) (2)
where Wsen is a H*(L*K) dimensional matrix and bsen is a H*1 dimensional bias vector; H refers to
the number of neurons in the hidden layer.
3.3.1. ENTITY-DRIVEN MECHANISM
Firstly, we conduct vector summation operation for each nouns’ word embedding to generate
hentity formulated as:
Hentity=ewNN1 ⊕ ewNN2⊕……⊕ ewNNk (3)
Then, we conduct element wise multiplication operation between hc and hentity.
The value of the output layer can be formulated as:
P(yc=1)=sigmod(UT
qc+b) (4)
where U is an H*1 vector and b denotes the bias; yc with value 1 means the text is coherent, and 0
otherwise.
Therefore, the total coherence score for a given document is the probability that all cliques within
the document are coherent, which is given by:
Sd=∏∈
=
dC
cyp )1( (5)
Finally, we can determine whether a text is coherent according to the value of their coherence
score.

5
3.4. TRAINING AND OPTIMIZATION
The cost function for the model is given by:
2
0
1
( )
2C trainset
Q
J H
M M θ
θ
∈ ∈Θ
Θ = +∑ ∑ (6)
0 log[ ( 1)] (1 )log[1 ( 1)]c c c cH y p y y p y= − = − − − = (7)
where Θ =[WRecursive,Wsen,Usen]; M denotes the number of training samples.
We adopt the widely applied optimization diagonal variant of AdaGrad (Duchi et al.[25]
) to
optimize the loss function.
4. EXPERIMENTS
In this section, we demonstrate the effectiveness of our discourse coherence model through both
sentence ordering and machine translation coherence rating tasks. The former aims to discern an
original text from a permuted ordering of its sentences, while the latter aims to discern a human
or reference translation from automatically machine generated translation.
4.1. DATASET
Sentence Ordering Dataset: We select documents for Chinese Treebank 6.0 from Linguistic
Data Consortium (LDC) with catalog number LDC2007T36 and ISBN1-58563-450-6. We select
the 100 documents from chtb_2946 to chtb_3045 as our training dataset, and 100 documents from
chtb_3046 to chtb_3145 as our testing dataset. The sentences in each source file will be
permutated at most 20 times. The total number of testing texts is 1027.The average number of
sentence are 10.33 and 13.56 for training set and testing set, respectively. In the evaluation, we
consider the original texts are more coherent (positive instances) than the permutated ones
(negative instances).
Machine Translation Dataset: Similarly, we extract documents for NIST Open Machine
Translation 2008 Evaluation (MT08) Selected Reference and System Translations from
Linguistic Data Consortium (LDC) with catalog number LDC2010T01 and ISBN1-58563-533-2.
Therein, the English-to-Chinese language pairs have 127 documents with 1830 segments, output
from 11 machine translation systems. The average number of sentence are 13.38 and 13.39 for
training set and testing set, respectively. In evaluation, we consider the human or reference
translation texts are more coherent than the machine generated one.
4.2. EXPERIMENTAL SETTINGS
Initialization: Similar to Li et al.[18]
, the parameter Wsen, Wrecursive and h0are initialized by
randomly drawing from the uniform distribution. The number of hidden layer H is set to
100.Learning rate in the optimization process is set to 0.01, and batch size is set to 20.
Differently, word embedding {e} for Chinese are trained using word2vec and Glove respectively.
The dimension for word embedding is 50 or 100. The window size L is 3 or 5.

6
Evaluation Metric: We report system’s performance using accuracy, which is the ratio of the
number of the selected original text/translation document divided by the total number of
texts/translation document.
Baseline System 1: Entity graph based model[14]
which has been demenstrated as a simple but
effective implementation of the entity-based coherence model. We re-implement their method in
this paper based on publicly available code3
.
Baseline System 2: Another baseline, Li et al.[18]
’s recursive neural model, which did not
consider the entity transition information.We transplant there English discourse coherence
framework to Chinese situation. Furthermore, we successfully integrate the entity information
into their deep model.
In addition, we employ Stanford parser4
to generate sentence-level constitute parser tree and
generate the part-of-speech to get the entities (nouns) occur in each sentence, and use utility
ICTCLAS5
to conduct Chinese word segmentation.
4.3. EXPERIMENT RESULTS
In this section, we report the experiment results for the Chinese discourse coherence modeling on
both sentence ordering and machine translation coherence rating task.
4.3.1. RESULTS ON SENTENCE ORDERING
Table 1 shows the performance of our entity-driven deep model using different windows size,
different dimension, and with different type of word embedding.
Table 1: The performance under different settings on sentence ordering task.
As shown in Table 1, it shows that:
(1)Dimension
Generally speaking, the performance increases with the increment of the dimension. In fact, the
larger the dimension, the more representative ability it is.
(2) Window size
The performance decreases with the increment of the window size, and the best performance
yields at the window size with 3. It is mostly caused by the local entity distribution characteristic
demenstrated by Barzilay and Lapata[11][12]
,Guinaudeau and Strube[14]
. As the increment of the
number of the window size, the entity co-occurance decreases accordingly.
3
http://github.com/karins/CoherenceFramework.
4
http://nlp.stanford.edu/software/lex-parser.shtml.
5
http://ictclas.nlpir.org/downloads

7
Table 2, below, lists the performance comparision among our model and the current baseline
model (traditional model and neural network model).
Table 2: Performance comparison among different coherence model on sentence ordering task;
Performance that is significantly superior to baseline systems (p<0.05, using paired t-test for significance)
is denoted by *.
It shows that our combined model significantly outperforms the current deep model for Chinese
discourse coherence modeling, which demonstrates the effectiveness and importance of the entity
distribution across sentences. Interestingly, the traditional entity based model also works for
Chinese discourse coherence evaluation, which doesn’t work fine for English situation. This is
mostly caused by the entity distribution are obvious in Chinese discourse than in English text.
4.3.2. RESULTS ON MACHINE TRANSLATION COHERENCE RATING
Table 3, below, lists the performance of our model and the baseline model.
Table 3: Performance comparison among different coherence model on machine translation
coherence rating task with dimension equals to 100; Performance that is significantly superior to baseline
systems (p<0.05, using paired t-test for significance) is denoted by *.
In fact, discourse coherence evaluation for the machine translation task is more common than the
sentence ordering task evaluation. As the results show in Table 3, again, our model significantly
outperforms the current model. Also, our model significantly outperforms the traditional entity-
based model. It is mostly caused by the entity distribution is not obvious in the text generated by
the machine. But the entity (nouns) information still can be integrated into current recursive
neural network model.
5. CONCLUSIONS AND FUTURE WORK
In this paper, we present an entity-driven recursive deep model for Chinese discourse coherence
modeling. We successfully integrate the entities across each sentence into current recursive neural
framework. Evaluation results on both sentence ordering and machine translation coherence
rating task show the effectiveness of the proposed model. Our future work is to integrate the co
reference mechanism into current combined recursive neural network model, together with other
coherence evaluation task.

8
ACKNOWLEDGEMENTS
The authors would like to thank the anonymous reviewers for their comments on this paper. This
research was supported bythe National Natural Science Foundation of China under Grant
No.61402208, No.61462045, No.61462044, No.61662030, the Natural Science Foundation and
Education Department of Jiangxi Province under Grant No. 20151BAB207027 and GJJ150351,
and the Research Project of State Language Commission under Grant No.YB125-99.
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AUTHORS
Fan Xu holds a Doctoral Degree (Ph.D.) in Computer Science from Soochow University,
China. His areas of research interest includes Natural Language Processing, Chinese
Information Processing, Discourse Analysis, and Speech Recognition. At present he is
working as Lector, School of Computer Information Engineering, Jiangxi Normal
University, China. He is member of various professional bodies including ACL, IEEE, and
ACIS.
Shujing Duis a Master of Computer Science of Jiangxi Normal University, China. Her
research interest includes Natural Language Processing, Chinese Information Processing,
Discourse Analysis
Maoxi Li holds a Doctoral Degree (Ph.D.) in Computer Science from Chinese Academy of
Sciences. His areas of research interest includes Machine Translation and Natural Language
Processing. At present he is working as Associate Professor, School of Computer
Information Engineering, Jiangxi Normal University, China.
Mingwen Wang holds a Doctoral Degree (Ph.D.) in Computer Science from Shanghai
Jiaotong University, China. His areas of research interest includes Machine Learning,
Information Retrieval, Natural Language Processing, Image Processing, and Chinese
Information Processing. At present he is working as Professor, School of Computer
Information Engineering, Jiangxi Normal University, China. He is member of various
professional bodies including ACL, IEEE, CCF, and ACIS.

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