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ALGORITHMIC TEXTUAL PRACTICES: IMPROVING FLUENCY AND WORD ORDER IN NEURAL MACHINE TRANSLATION OUTPUT.

1. Introduction

Neural machine translation constitutes a substitute to the statistical machine translation pattern in diverse languages. (Eriguchi et al., 2019) The prevailing neural machine translation patterns commonly employ word-level modeling to insert input sentences into semantic space (Balica, 2018; Cruciani, 2018; Fabricio, 2016; Lazaroiu, 2018; Mihaila and Mateescu, 2017; Nica, 2018; Popescu, 2017a, b; Radulescu, 2017), but that may not be adequate for the encoder modeling of neural machine translation, particularly for languages where tokenizations are typically confusing, as there may be tokenization inaccuracies which may adversely shape the encoder modeling of neural machine translation, while the adequate tokenization granularity is ambiguous. (Tan et al., 2018)

2. Conceptual Framework and Literature Review

Neural machine translation represents a groundbreaking model, and the attention mechanism (Bolton et al., 2018; Dicu, 2018; Farber, 2017; Machan, 2017; Mihaila, 2018; Nica et al., 2018; Popescu et al., 2017; Sion, 2018a, b) is the prominent method with the cutting-edge records in numerous language pairs. All options of the attention mechanism employ only temporal awareness where one scalar value is allocated to one context vector matching a source word. (Choi et al., 2018) Undeveloped neural machine translation patterns are contingent on sequence-to-sequence learning that expresses in code a sequence of source words into a vector space and produces an additional sequence of target words from the vector: sentences are interpreted just as sequences of words with no internal structure. (Eriguchi et al., 2019) The naive back-translation method enhances the translation performance considerably, but its application for monolingual input is not relevantly valid as established neural machine translation patterns do not differentiate between the genuine parallel corpus and the back translated synthetic parallel one. (Yang et al., 2019)

3. Methodology and Empirical Analysis Building my argument by drawing on data collected from the Boston Consulting Group, Deloitte, eMarketer, Locaria, MIT Sloan Management Review, NCSC, and Statista, I performed analyses and made estimates regarding awareness and usage of translation applications featuring machine learning (%), how professional translators worldwide see artificial intelligence affecting their work in the future (%), and market size of the global language services industry (billion U.S. dollars). The results of a study based on data gathered from 4,200 respondents provide support for my research model. Employing the structural equation modeling and using the probability sampling technique, I collected and inspected data via a self-administrated questionnaire.

4. Results and Discussion

Current developments of neural machine translation are instrumental in performing translation by employing an uncomplicated end-to-end architecture. (Eriguchi et al., 2019) Similarly as any machine learning algorithms, neural networks represent a definite class of statistical learning patterns. (Petrucci et al., 2018) Machine translation is presently going through a paradigm repositioning from statistical to neural network patterns. (Moorkens, 2018) Neural machine translation generates significantly fewer lexical, morphology, and word order errors, better configurations in the rearrangement of verbs and nouns, but produces more inaccuracies in the translation of proper nouns than phrase-based approaches. (Bentivogli et al., 2018) Nearly all applications of neural machine translation patterns use a padding approach when processing a mini-batch to alter to the same length all sentences in it. (Qiao et al., 2018) Statistical machine translation systems are disadvantaged by fluency errors, notably as grammatical inaccuracies and ones concerning idiomatic word choices. (Tezcan et al., 2019) (Tables 1-6)

5. Conclusions and Implications

Neural machine translation is ordinarily enhanced to produce sentences which comprise n-grams with ground target to the fullest extent, but n-gram precisions, the physically fashioned approximate loss function, can misdirect the pattern to create suboptimal translations. (Yang et al., 2018) Handling the ontology learning problem as a neural machine translation assignment may be a sound manner to approach perpetual expressive ontology learning tasks (Bratu, 2018; Donnelly, 2017; King, 2017; Michailidou, 2018; Nica, 2017; Peters, 2017; Popescu, 2018; Skordoulis, 2016; Smith and Stirling, 2018), such as language inconsistency, domain self-determination, and significant engineering expenses. (Petrucci et al., 2018) Neural networks integrate frame of reference from the training input, source text, and elaborating target text and mostly generate words in the adequate context, although systems are nevertheless inconstant and necessitate human review. (Taivalkoski-Shilov, 2018)

Funding

This paper was supported by Grant GE-1287672 from the Cognitive Labor Institute, New York, NY.

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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ADINA RADULESCU

adina.radulescu@spiruharet.ro

Spiru Haret University, Bucharest, Romania

How to cite: Radulescu, Adina (2019). "Algorithmic Textual Practices: Improving Fluency and Word Order in Neural Machine Translation Output," Linguistic and Philosophical Investigations 18: 126-132.

Received 17 December 2018 * Received in revised form 18 March 2019

Accepted 19 March 2019 * Available online 14 April 2019

doi:10.22381/LPI1820198
Table 1 Awareness and usage of smartphone applications featuring
machine learning (%)

                                Awareness  Usage

Predictive text                    54       38
Route suggestions                  40       26
Voice assistants                   39       15
Voice search                       35       14
Translation apps                   31       16
Voice-to-text                      27       12
Email classification               26       11
Automated calendar entries         22        9
Location-based app suggestions     21       11
Automated photo classification     21        8

Sources: Deloitte; Statista; my survey among 4,200 individuals
conducted November 2018.

Table 2 Market size of the global language services industry (billion
U.S. dollars)

2009  23.5
2010  26.3
2011  28.34
2012  33.05
2013  34.78
2014  37.19
2015  38.16
2016  40.27
2017  43.08
2018  46.52
2019  49.14

Sources: Statista; my estimates.

Table 3 Do you utilize remote interpreting technology? (Check all that
apply, %)

Audio or telephonic interpretation (standard telephone)  88
Video conferencing                                       61
Video remote interpreting                                52
Web-based applications (e.g. Skype)                      48
Specialized telephone equipment                          34
Other technological solutions (please provide details)   23
Voice over internet protocol                             21
Translation software and automated interpreter software  18
N/A                                                       7

Sources: NCSC; my survey among 4,200 individuals conducted November
2018.

Table 4 Levels of understanding for artificial intelligence-related
technology and business context: To what extent do you agree with the
following statements about your organization? (%)

              We understand...      Pioneers  Investigators

Technology    Required                 92          78
implications  technological
              breakthroughs to
              succeed with
              artificial
              intelligence
Technology    Data required for        87          76
implications  artificial
              intelligence
              algorithm
              training
Technology    Processes for            86          70
implications  artificial
              intelligence
              algorithm
              training
Business      Artificial               92          84
implications  intelligence-
              related changed
              ways of business
              value generation
Business      Development              85          74
implications  time of artificial
              intelligence-
              based products
              and services
Business      Development              83          75
implications  costs of artificial
              intelligence-
              based products
              and services
Workplace     Required changes         84          84
implications  of knowledge and
              skills for future
              artificial
              intelligence needs
Workplace     Effect of artificial     87          78
implications  intelligence in the
              workplace on
              organization's
              behavior
Industry      Artificial               83          83
implications  intelligence-
              related shift of
              industry power
              dynamics

              Experimenters  Passives

Technology          27          15
implications
Technology          24          11
implications
Technology          20           7
implications
Business            33          22
implications
Business            21          18
implications
Business            16          10
implications
Workplace           22          19
implications
Workplace           18          18
implications
Industry            22          18
implications

Sources: MIT Sloan Management Review; The Boston Consulting Group; my
survey among 4,200 individuals conducted November 2018.

Table 5 How professional translators worldwide see artificial
intelligence affecting their work in the future (%)

No impact whatsoever                        4
Less work for professional translators     46
Same volume but higher standards expected  37
Overall volume of work will grow           13

Sources: MIT Sloan Management Review; The Boston Consulting Group; my
survey among 4,200 individuals conducted November 2018.
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