https://doi.org/10.31449/inf.v45i6.3728 Informatica 45 (2021) 115–118 115 
English Semantic Recognition Based on an Intelligent Algorithm 
Na Deng 
International Education School,  Huanghuai University, Zhumadian, Henan 463000, China 
E-mail: n79597@yeah.net 
Keywords: intelligent algorithm, English translation, semantic recognition, transformer model, BLEU 
Received: September 3, 2021 
In the process of translation, semantic barriers have attracted extensive attention from researchers. 
Taking the translation between Chinese and English as an example, this paper used intelligent algorithms 
to recognize the semantic role of English, introduced the semantic role labeling, designed a semantic role 
encoder, integrated the encoder with the transformer model, and tested the translation performance of the 
system. The experimental results showed that the BLEU-4 score of the combined system was significantly 
higher than the baseline system and the traditional transformer system. The average BLEU-4 values of 
the three systems were 35.02, 35.78, and 36.9, respectively, and the score of the combined system was the 
highest. The specific analysis of several examples also found that the translation results of the combined 
system were more reliable. The experimental results verify the effectiveness of the combined system in 
machine translation and the importance of semantic recognition in translation. 
Povzetek: Pri prevajanju med angleščino in kitajščino pomaga inteligentni semantični algoritem. 
 
1 Introduction 
Machine translation refers to translating one natural 
language into another through machine tools. With the 
development of technology, machine translation is also 
improving, which has made a great contribution to the 
communication of social culture and information, and 
more and more methods have been applied in machine 
translation. Sennrich et al. [1] encoded rare and unknown 
words into subword unit sequences, i.e., translate various 
categories of words by units smaller than words. Through 
the comparison on WMT 15 translation tasks, they found 
that subword models improved over a back-off dictionary 
baseline for the WMT 15 translation tasks English-
German and English-Russian by 0.8 and 1.5 BLEU, 
respectively. Aiming at the problem of disambiguation in 
translation, Choi et al. [2] designed a method of 
contextualizing the word embedding vectors using a 
nonlinear bag-of-words representation of the source 
sentence and proposed to represent special tokens with 
using typed symbols. Experiments showed that the 
method could significantly improve the quality of 
translation. For neural machine translation, Luong et al. 
[3] trained a neural machine translation (NMT) system on 
data that was augmented by the output of a word alignment 
algorithm, allowing the NMT system to output, for each 
OOV word in the target sentence, its corresponding word 
in the source sentence. Experiments on WMT'14 English 
to French translation tasks showed that the method 
achieved an improvement of 2.8 BLEU. Lee et al. [4] 
mapped the source character sequence to the target 
character sequence without any segmentation. They 
adopted the character-level convolution network with 
maximum pooling on the encoder to improve the speed of 
model training. Through experiments, they found that the 
method had higher translation quality. Most of the current 
machine translations need to be adjusted manually to 
achieve high readability [5]. One of the major problems is 
the obstacle of semantic recognition in machine 
translation [6]. Almost all language behaviors of people 
are related to semantics. In translation activities, the size 
of the obstacles to semantic recognition depends on the 
translator’s mastery of the source language and the target 
language. The higher the mastery degree is, the smaller the 
obstacles to semantic recognition in the process of 
translation are. This is for manual translation. In machine 
translation, the machine will not be affected by emotion, 
language, etc. If the relevant knowledge input into the 
system is perfect enough and the learning mechanism is 
intelligent enough, the obstacles of semantic recognition 
will be smaller. Based on semantic recognition, this study 
analyzed the method of Chinese-English machine 
translation and carried out experiments on the designed 
system to understand the reliability of the system in 
translation and make some contributions to the better 
development of machine translation. 
2 Neural machine translation 
combined with semantic roles 
2.1 Semantic role labeling 
Machine translation, with the advantages of high speed 
and no need for manual work, has attracted extensive 
attention in translation work. Neural machine translation 
is the mainstream [7], but it can only learn from bilingual 
parallel corpus, ignoring linguistic knowledge, which 
leads to the poor quality of translation [8]. The natural 
language contains a lot of fuzziness, near meaning, and 
polysemy [9]. Therefore, semantic recognition is of great 

116 Informatica 45 (2021) 115–118  N. Dang 
value to NMT. In the current research, semantic 
annotation and recognition are of great help to 
disambiguation and role understanding [10]. Among 
them, semantic role annotation (SRL) [11] is an important 
content, which can realize shallow semantic analysis and 
identify the labels of argument, agent, patient, etc. in a 
sentence. SRL is helpful for the computer to better 
understand the true meaning of a sentence. An example is 
as follows. 
[Alice] Agent [met] Predicate [Bob] Patient in the 
[church] Location [yesterday] time [evening] time. 
In this sentence, [Alice] is the agent, [yesterday] and 
[evening] are the time, [church] is the location, [met] is the 
predicate, and [Bob] is the patient. 
In the translation method designed in this paper, the 
AllenNLP tool [12] is used to realize the SRL of the source 
corpus. According to the number of predicates in a 
sentence, the sentence is divided into the corresponding 
number of lines. Each line includes the predicate label and 
its corresponding semantic role label. The prefix “BIO” is 
used for further modification. “B -” means that the current 
word is the first word within the scope of the semantic 
role, “I -” means that the current word is a middle word or 
a tail word, and “O -” means that the current word does 
not belong to any semantic role. 
2.2 Transformer model combined with 
semantic role encoder 
The characteristic of NMT is that it has an encoder and a 
decoder. The encoder reads the source sequence and 
outputs the vector; then, the decoder generates the correct 
translation according to the source vector. The specific 
methods include convolution neural networks [13], deep 
neural networks [14], etc. The transformer model is one 
kind of NMT [15], which only uses the attention 
mechanism to encode and decode and comprises an 
attention mechanism and a feedforward neural network. In 
one operation of the attention mechanism, variables 
Query, Key, and Value, i.e., Q, K, and V, are involved, 
and the operation process is: 
Attention (Q, K, V) = softmax (
QK
T
√d
k
)V, 
where 𝑑 𝑘 refers to the dimension of the model. 
In order to make up for the loss of the input sequence 
order, the transformer model introduces the concept of 
position-coding: 
PE(pos , 2i) = sin (pos /10000
2i/d
model
,  
PE(pos , 2i + 1) = cos (pos /10000
2i/d
model
,  
where pos refers to the position of the input word, i is a 
bit of the position vector, and 𝑑 𝑚𝑜𝑑𝑒𝑙 is the dimension of 
the model. In the process of translation, the encoder learns 
different features through the attention module, and the 
decoder decodes the semantic vector. Through softmax 
operation, the target language words can be generated one 
by one. 
A semantic role encoder is used to combine semantic 
recognition with the transformer model. For a source 
sentence, X = [x
1
, x
2
, … , x
n
], there is a same semantic role 
label sequence: L = [l
1
, l
2
, … , l
n
] . The transformer 
encoder is composed of 𝑁 𝑠 same layers. Every layer 
includes two sublayers. The calculation formula of the 
first sublayer is: 
𝐴 𝑛 = 𝑀𝑢𝑙𝑡𝑖𝐻𝑒𝑎𝑑 (𝑆 𝑛 −1
, 𝑆 𝑛 −1
, 𝑆 𝑛 −1
), 
where 𝑆 𝑛 −1
 refers to the output of the n-1-th layer of the 
encoder. After residual connection and layer 
regularization processing, there is: 
𝐵 𝑛 = 𝐿𝑎𝑦𝑒𝑟𝑁𝑜𝑟𝑚 (𝐴 𝑛 + 𝑆 𝑛 −1
). 
Then, the second sublayer is a bit-by-bit fully 
connected feedforward neural network, which is also 
processed by residual connection and layer regularization: 
C
n
= FFN(B
n
), 
S
n
= LayerNorm (B
n
+ C
n
). 
The encoder that fuses the semantic role has only one 
layer, and the calculation formula is: 
𝐴 𝑆𝑅𝐿
= 𝑀𝑢𝑙𝑡𝑖𝐻𝑒𝑎𝑑 (𝐿 , 𝐿 , 𝑆 𝑁 𝑠 ), 
where 𝐿 refers to the label sequence of the semantic role, 
S
N
S
 is the final output of the source encoder, and A
SRL
 
refers to the final output of the semantic encoder. Finally, 
the two outputs are fused by the gating mechanism. The 
calculation formulas are: 
γ = σ(W
N
s
S
N
S
+ W
SRL
S
SRL
), 
𝑆 𝑚𝑖𝑥 = 𝛾 ⊙ 𝑆 𝑁 𝑆 + (1 − 𝛾 ) ⊙ 𝑆 𝑆𝑅𝐿
, 
where W
N
S
 and W
SRL
 are the matrices of gating 
parameters, and S
mix
 is the mixture of the original 
sentence and the semantic role information as the input of 
the decoder. 
3 Experimental analysis 
3.1 Experimental data 
The Chinese-English bilingual corpus used in the 
experiment contains about four million parallel sentence 
pairs, which come from some subsets of the LDC corpus, 
including LDC2003E14, LDC2004E12, etc. The GIZA++ 
tool was used for word alignment, and then the grow-diag-
final-and heuristic method was used to obtain the 
information of word alignment. The development set of 
the experiment was the evaluation corpus of NIST2005 
(NIST05), and the test sets were the evaluation corpus of 
NIST2002, 2003, 2004, 2005, and 2008 (NIST02, 
NIST03, NIST04, NIST05, and NIST08). The hierarchical 
phrase translation system [16] was used as the baseline 
system and compared with the traditional transformer 
system and the transformer system combined with 
semantic role recognition. 
3.2 Evaluation index 
This paper used BLEU [17] to automatically evaluate the 
quality of machine translation. BLEU index was based on 
N-ary grammar. First, the maximum frequency of a word 
in different results was recorded; then, the frequency of 
each candidate word in translation was corrected. The 
calculation formula is: 
𝑃 𝑛 =
∑ ∑ 𝑐𝑜𝑢𝑛𝑡 𝑐𝑙𝑖𝑝 (𝑁 −𝑔𝑟𝑎𝑚 )
𝑁 −𝑔𝑟𝑎𝑚 ∈𝑐 𝑐 ∈{ 𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝑠 }
∑ ∑ 𝑐𝑜𝑢𝑛𝑡 𝑐𝑙𝑖𝑝 (𝑁 −𝑔𝑟𝑎𝑚 ′)
𝑁 −𝑔𝑟𝑎𝑚 ∈𝑐 ′ 𝑐 ′∈{ 𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝑠 }
,  
where c is the total length of the candidate translation 
sentence. It is assumed that the total length of the reference 
translation is r. There is also a length penalty term in 
BLEU: 

English Semantic Recognition Based on Intelligent Algorithm Informatica 45 (2021) 115–118 117 
BP = {
1, c > r
e
1−
r
c
, c ≤ r
. 
Finally, the calculation formula of BLEU is: 
BLEU = BP × exp (∑ w
n
log p
n
N
n=1
), 
where N refers to the maximum order of N-ary grammar 
and 𝑤 𝑛  is the weight coefficient. In the experiment, 
BLEU-4 was used for evaluation, i.e., N = 4, w
n
=
1
N
.  
3.3 Experimental results 
On different test sets, the BLEU-4 values of the two 
systems are shown in Figure 1. 
It was seen from Figure 1 that the transformer system 
performed better in translation than the baseline system, 
and the translation performance of the transformer system 
further improved after combining with semantic role 
recognition. On NIST 02, the BLEU-4 scores of the three 
systems were 37.2, 38.1, and 39.7, respectively, and the 
BLEU-4 value of the transformer system combined with 
semantic role recognition was 6.72 % higher than that of 
the baseline system and 4.20 % higher than that of the 
transformer system. On NIST 03, the BLEU-4 scores of 
the three systems were 36.6, 37.2, and 38.4, respectively, 
and the BLEU-4 value of the transformer system 
combined with semantic role recognition was 4.92 % 
higher than that of the baseline system and 3.23 % higher 
than that of the transformer system. On NIST 04, the 
BLEU-4 scores of the three systems were 38.1, 39.2, and 
40.1, respectively, and the BLEU-4 value of the 
transformer system combined with semantic role 
recognition was 5.25 % higher than that of the baseline 
system and 2.30 % higher than that of the transformer 
system. On NIST 05, the  BLEU-4 values of the three 
systems were 35.4, 36.1, and 37.2, respectively; the 
BLEU-4 value of the transformer system combined with 
semantic role recognition was 5.08% higher than that of 
the baseline system and 3.05% higher than that of the 
transformer system. On NIST08, the BLEU-4 values of 
the three systems were 27.8, 28.3, and 29.1, respectively; 
the BLEU-4 value of the transformer system combined 
with semantic role recognition was 4.68% higher than that 
of the baseline system and 2.83% higher than that of the 
transformer system. 
The average BLEU-4 values of the three systems were 
calculated, and they were 35.02, 35.7, and 36.9, 
respectively. It was found that the transformer system 
combined with semantic role recognition had the highest 
BLEU-4 value and the highest translation quality in the 
process of the English translation. Finally, the translation 
performance of the system was analyzed taking several 
sentences as the example. 
As shown in Table 1, due to semantic differences, 
there are some differences in translation results. In 
example 1, “charges” has multiple meanings, such as fee 
collection, fee, accusation, rushing, etc.; in the output of 
the first two systems, it was translated as “fee collection”, 
but in the transformer system combined with semantic role 
recognition, it was translated as “charge”. Combined with 
context and semantics, the translation of the transformer 
system combined with semantic role recognition was 
correct. In example 2, “memory” means something that is 
remembered, internal storage, etc.; in this sentence, it 
should mean “internal storage”. In the first two systems, it 
was wrongly translated as “something that is 
remembered” because the two systems did not recognize 
the semantics, which led to the wrong choice of candidate 
words. In example 3, “application” means application, 
complaint, use, application program, etc.; in this sentence, 
“application program” was correct. 
It was found from Figure 1 and Table 1 that the 
transformer system combined with semantic role 
recognition showed better performance in the process of 
 
Figure 1: Comparison of bLEU-4 values. 
System Sentence 
Original sentence Charges against four other 
men were found not 
proven. 
Baseline system 对四名其 他男子 的 收 费
没有被证 明。 
Transformer system 对四名男 子的 收 费 被发
现没有证 实。 
The transformed 
system combined with 
semantic role 
recognition 
对四名男 子的指控被发
现没有被 证实。 
Original sentence The data are stored in the 
computer’s memory. 
Baseline system 数据被存 储在计 算机的
记忆 中。 
Transformer system 数据存 储 在计算 机的 记
忆 中。 
The transformed 
system combined with 
semantic role 
recognition 
数据存 储 在计算 机的 内
存 中。 
Original sentence After a full test, he 
submitted his application. 
Baseline system 经过全面 测试后 ，他提
交了 申 请 。 
Transformer system 经过全面 测试， 他提交
了他的申 请 。 
The transformed 
system combined with 
semantic role 
recognition 
经过全面 测试， 他提交
了他的应用程序 。 
Table 1: Example sentence analysis. 

118 Informatica 45 (2021) 115–118  N. Dang 
translation and produced more accurate and reliable 
results. 
4 Conclusion 
For Chinese-English machine translation, this study 
designed a new system based on the encoder and 
transformer model, which combined semantic role 
recognition, and tested it. It was found that the designed 
system had better performance, the highest BLEU score, 
and more accurate translation results, compared with the 
baseline system and the traditional transformer system. 
This work contributes the further development of machine 
translation and also helps to improve the importance of 
semantic recognition in translation work. 
References 
[1] Rico Sennrich, Barry Haddow, and Alexandra Birch. 
Neural Machine Translation of Rare Words with 
Subword Units. Computer Science, 1715-1725, 2016. 
[2] Heeyoul Choi, Kyunghyun Cho, and Yoshua Bengio. 
Context-Dependent Word Representation for Neural 
Machine Translation. Computer Speech & Language, 
45(Sep.):149-160, 2017. 
https://doi.org/10.1016/j.csl.2017.01.007 
[3] Minh-Thang Luong, Ilya Sutskever, Quoc V. Le, 
Oriol Vinyals, and Wojciech Zaremba. Addressing 
the Rare Word Problem in Neural Machine 
Translation. Bulletin of University of Agricultural 
Sciences and Veterinary Medicine Cluj-Napoca. 
Veterinary Medicine, 27(2):82-86, 2015. 
https://doi.org/10.3115/v1/P15-1002 
[4] Jason Lee, Kyunghyun Cho, and Thomas Hofmann. 
Fully Character-Level Neural Machine Translation 
without Explicit Segmentation. Transactions of the 
Association for Computational Linguistics, 5:365-
378, 2017. https://doi.org/10.1162/tacl_a_00067 
[5] Jernej Vicic. A fast implementation of rules based 
machine translation systems for similar natural 
languages. Informatica, 37(4):455-456, 2013. 
[6] Mohamed El Bachir Menai. Word sense 
disambiguation using an evolutionary approach. 
Informatica, 38(2):155-169, 2014. 
[7] Castilho Sheila, Moorkens Joss, Gaspari Federico, 
Calixto Iacer, Tinsley John, and Way Andy. Is Neural 
Machine Translation the New State of the Art?. The 
Prague Bulletin of Mathematical Linguistics, 
108(108):109-120, 2017. 
https://doi.org/10.1515/pralin-2017-0013 
[8] Philip Arthur, Graham Neubig, and Satoshi 
Nakamura. Incorporating Discrete Translation 
Lexicons into Neural Machine Translation. Computer 
Science, eprint arXiv:1606.02006, 2016. 
https://doi.org/10.18653/v1/D16-1162 
[9] Chong Chai Chua, Tek Yong Lim, Lay-Ki Soon, 
Enya Kong Tang, and Bali Ranaivo-Malançon. 
Meaning preservation in Example-based Machine 
Translation with structural semantics. Expert Systems 
with Applications, 78(JUL.):242-258, 2017. 
https://doi.org/10.1016/j.eswa.2017.02.021 
[10] Gang Zhang. Research on the efficiency of intelligent 
algorithm for english speech recognition and sentence 
translatione. Informatica, 45(2):309-314, 2021. 
https://doi.org/10.31449/inf.v45i2.3564 
[11] Yoko Nakajima, Michal Ptaszynski, Hirotoshi 
HONMA, and Fumito Masui. A Method for 
Extraction of Future Reference Sentences Based on 
Semantic Role Labeling. IEICE Transactions on 
Information & Systems, E99.D(2):514-524, 2016. 
https://doi.org/10.1587/transinf.2015EDP7115 
[12] Matt Gardner, Joel Grus, Mark Neumann, Oyvind 
Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, 
Michael Schmitz, and Luke Zettlemoyer. AllenNLP: 
A Deep Semantic Natural Language Processing 
Platform. Proceedings of Workshop for NLP Open 
Source Software (NLP-OSS), 1-6, 2018. 
https://doi.org/10.18653/v1/W18-2501 
[13] Taiki Watanabe, Akihiro Tamura, Takashi Ninomiya, 
and Teguh Bharata Adji. Neural Machine Translation 
with CKY-based Convolutional AttentionCKY に基
づ く 畳 み 込 み ア テ ン シ ョ ン 構 造 を 用 い た ニ ュ ー
ラル機械翻訳. Journal of Natural Language 
Processing, 26(1):207-230, 2019. 
https://doi.org/10.5715/jnlp.26.207 
[14] Jiajun Zhang, and Chengqing Zong. Deep Neural 
Networks in Machine Translation: An Overview. 
IEEE Intelligent Systems, 30(5):16-25, 2015. 
https://doi.org/10.1109/MIS.2015.69 
[15] Chao Su, Heyan Huang, Shumin Shi, Ping Jian, and 
Xuewen Shi. Neural machine translation with 
Gumbel Tree-LSTM based encoder. Journal of 
Visual Communication and Image Representation, 
71:102811, 2020. 
https://doi.org/10.1016/j.jvcir.2020.102811 
[16] Xiaoyin Fu, Wei Wei, Lichun Fan, Shixiang Lu, a nd 
Bo Xu. Nesting hierarchical phrase-based model for 
speech-to-speech translation. 2012 8th International 
Symposium on Chinese Spoken Language Processing 
(ISCSLP), 368-372, 2012. 
https://doi.org/10.1109/ISCSLP.2012.6423497 
[17] Yaya Tian, Shaweta Khanna, Anton Pljonkin. 
Research on machine translation of deep neural 
network learning model based on ontology. 
Informatica, 45(5):643-649, 2021. 
https://doi.org/10.31449/inf.v45i5.3559