Is the Corpus Ready for Machine Translation? A Case Study with Python to Pseudo-Code Corpus
The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using su...
Saved in:
| Published in | Arabian journal for science and engineering Vol. 48; no. 2; pp. 1845 - 1858 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2193-567X 1319-8025 2191-4281 2191-4281 |
| DOI | 10.1007/s13369-022-07049-0 |
Cover
| Abstract | The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using suitable evaluation measures. However, the performance of underlying learning algorithms can be greatly influenced by the correctness and the consistency of the corpus. We present our investigations for the relevance of a publicly available python to pseudo-code parallel corpus for automated documentation task, and the studies performed using this corpus. We found that the corpus had many visible issues like overlapping of instances, inconsistency in translation styles, incompleteness, and misspelled words. We show that these discrepancies can significantly influence the performance of the learning algorithms to the extent that they could have caused previous studies to draw incorrect conclusions. We performed our experimental study using statistical machine translation and neural machine translation models. We have recorded a significant difference (
∼
10% on BLEU score) in the models’ performance after removing the issues from the corpus. |
|---|---|
| AbstractList | The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using suitable evaluation measures. However, the performance of underlying learning algorithms can be greatly influenced by the correctness and the consistency of the corpus. We present our investigations for the relevance of a publicly available python to pseudo-code parallel corpus for automated documentation task, and the studies performed using this corpus. We found that the corpus had many visible issues like overlapping of instances, inconsistency in translation styles, incompleteness, and misspelled words. We show that these discrepancies can significantly influence the performance of the learning algorithms to the extent that they could have caused previous studies to draw incorrect conclusions. We performed our experimental study using statistical machine translation and neural machine translation models. We have recorded a significant difference (∼ 10% on BLEU score) in the models’ performance after removing the issues from the corpus. The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using suitable evaluation measures. However, the performance of underlying learning algorithms can be greatly influenced by the correctness and the consistency of the corpus. We present our investigations for the relevance of a publicly available python to pseudo-code parallel corpus for automated documentation task, and the studies performed using this corpus. We found that the corpus had many visible issues like overlapping of instances, inconsistency in translation styles, incompleteness, and misspelled words. We show that these discrepancies can significantly influence the performance of the learning algorithms to the extent that they could have caused previous studies to draw incorrect conclusions. We performed our experimental study using statistical machine translation and neural machine translation models. We have recorded a significant difference ( $$\sim $$ ∼ 10% on BLEU score) in the models’ performance after removing the issues from the corpus. The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using suitable evaluation measures. However, the performance of underlying learning algorithms can be greatly influenced by the correctness and the consistency of the corpus. We present our investigations for the relevance of a publicly available python to pseudo-code parallel corpus for automated documentation task, and the studies performed using this corpus. We found that the corpus had many visible issues like overlapping of instances, inconsistency in translation styles, incompleteness, and misspelled words. We show that these discrepancies can significantly influence the performance of the learning algorithms to the extent that they could have caused previous studies to draw incorrect conclusions. We performed our experimental study using statistical machine translation and neural machine translation models. We have recorded a significant difference ( 10% on BLEU score) in the models' performance after removing the issues from the corpus. The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using suitable evaluation measures. However, the performance of underlying learning algorithms can be greatly influenced by the correctness and the consistency of the corpus. We present our investigations for the relevance of a publicly available python to pseudo-code parallel corpus for automated documentation task, and the studies performed using this corpus. We found that the corpus had many visible issues like overlapping of instances, inconsistency in translation styles, incompleteness, and misspelled words. We show that these discrepancies can significantly influence the performance of the learning algorithms to the extent that they could have caused previous studies to draw incorrect conclusions. We performed our experimental study using statistical machine translation and neural machine translation models. We have recorded a significant difference ( ∼ 10% on BLEU score) in the models’ performance after removing the issues from the corpus. |
| Author | Rai, Sawan Belwal, Ramesh Chandra Gupta, Atul |
| Author_xml | – sequence: 1 givenname: Sawan orcidid: 0000-0002-2590-1097 surname: Rai fullname: Rai, Sawan email: sawanrai@iiitdmj.ac.in organization: PDPM Indian Institute of Information Technology Design and Manufacturing – sequence: 2 givenname: Ramesh Chandra surname: Belwal fullname: Belwal, Ramesh Chandra organization: PDPM Indian Institute of Information Technology Design and Manufacturing – sequence: 3 givenname: Atul surname: Gupta fullname: Gupta, Atul organization: PDPM Indian Institute of Information Technology Design and Manufacturing |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35874184$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkU1rFTEYhYNUbK39Ay4k4Hpqvj82Shm0FioWrSC4CJlJphOZJtckY7n_3um9t1ZdFFd5IeecN-fJU7AXU_QAPMfoGCMkXxVMqdANIqRBErFlegQOCNa4YUThvc1MGy7k131wVEroEFNUc4zpE7BPuZIMK3YAvp0VWEcP25RXc4GfvHVrOKQMP9h-DNHDy2xjmWwNKb6BJ7C1xcPPdV5UN6GO8GJdxxRhTfCi-Nmlpk3uLu0ZeDzYqfij3XkIvrx7e9m-b84_np61J-dNzxCrjRKd5KofuCWaCWeZVghTTK0gHPdKC6-5k1j4XiIhOupc1wnpOkoHzr0a6CGg29w5ruz6xk6TWeVwbfPaYGRuaZktLbPQMhtaBi2u11vXau6uvet9rNneO5MN5u-bGEZzlX4aTbTA5Dbg5S4gpx-zL9V8T3OOS1NDpGRcIEzkonrx55rf-XdfsAjUVtDnVEr2g-lD3fBetobp4QrkH-t_9d7RKos4Xvl8_-wHXL8AYYK6pQ |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2023_3302695 crossref_primary_10_1016_j_engappai_2023_107713 |
| Cites_doi | 10.1515/CLLT.2008.010 10.1145/3468264.3468611 10.3115/v1/W14-4012 10.1109/APSEC.2018.00101 10.21236/ADA460212 10.1162/neco.1989.1.2.270 10.1109/ASWEC.2018.00011 10.18653/v1/D13-1176 10.1109/ase.2015.36 10.1162/neco.1997.9.8.1735 10.1017/CBO9780511815829 10.1145/3340531.3412048 10.1145/1380584.1380586 10.1109/SERVICES-I.2009.56 10.1109/ICSE.2019.00087 10.1145/2487575.2487600 10.3115/v1/D14-1179 10.1007/978-3-319-18032-8_20 10.1016/0885-064X(88)90021-0 10.3115/1073083.1073135 10.1109/TCSS.2019.2956481 10.4324/9780203856949-35 10.1145/3488560.3502182 10.18653/v1/W17-3204 |
| ContentType | Journal Article |
| Copyright | King Fahd University of Petroleum & Minerals 2022 King Fahd University of Petroleum & Minerals 2022. Copyright Springer Nature B.V. 2023 |
| Copyright_xml | – notice: King Fahd University of Petroleum & Minerals 2022 – notice: King Fahd University of Petroleum & Minerals 2022. – notice: Copyright Springer Nature B.V. 2023 |
| DBID | AAYXX CITATION NPM 5PM ADTOC UNPAY |
| DOI | 10.1007/s13369-022-07049-0 |
| DatabaseName | CrossRef PubMed PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef PubMed |
| DatabaseTitleList | PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2191-4281 |
| EndPage | 1858 |
| ExternalDocumentID | 10.1007/s13369-022-07049-0 PMC9296120 35874184 10_1007_s13369_022_07049_0 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 203 2KG 406 AAAVM AACDK AAHNG AAIAL AAJBT AANZL AAPKM AARHV AASML AATNV AATVU AAUYE AAYTO AAYZH ABAKF ABBRH ABDBE ABDBF ABDZT ABECU ABFSG ABFTD ABFTV ABJNI ABJOX ABKCH ABMQK ABQBU ABRTQ ABSXP ABTEG ABTKH ABTMW ABXPI ACAOD ACBXY ACDTI ACHSB ACMDZ ACMLO ACOKC ACPIV ACSTC ACUHS ACZOJ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEJRE AEMSY AEOHA AESKC AEVLU AEXYK AEZWR AFBBN AFDZB AFHIU AFLOW AFOHR AFQWF AGAYW AGJBK AGMZJ AGQEE AGQMX AGRTI AHAVH AHBYD AHPBZ AHSBF AHWEU AIAKS AIGIU AILAN AITGF AIXLP AJBLW AJRNO AJZVZ ALFXC ALMA_UNASSIGNED_HOLDINGS AMXSW AMYLF AOCGG ATHPR AXYYD AYFIA BGNMA CSCUP DDRTE DNIVK DPUIP EBLON EBS EIOEI EJD ESX FERAY FIGPU FINBP FNLPD FSGXE GGCAI GQ7 H13 HG6 I-F IKXTQ IWAJR J-C JBSCW JZLTJ L8X LLZTM M4Y MK~ NPVJJ NQJWS NU0 O9J PT4 ROL RSV SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE TSG TUS UOJIU UTJUX UZXMN VFIZW ZMTXR ~8M AAYXX CITATION NPM 06D 0VY 23M 29~ 2KM 30V 408 5GY 96X AAJKR AARTL AAYIU AAYQN AAZMS ABTHY ACGFS ACKNC ADHHG ADHIR AEGNC AEJHL AENEX AEPYU AETCA AFWTZ AFZKB AGDGC AGWZB AGYKE AHYZX AIIXL AMKLP AMYQR ANMIH AYJHY ESBYG FFXSO FRRFC FYJPI GGRSB GJIRD GX1 HMJXF HRMNR HZ~ I0C IXD J9A KOV O93 OVT P9P R9I RLLFE S27 S3B SEG SHX T13 U2A UG4 VC2 W48 WK8 ~A9 5PM ADTOC UNPAY |
| ID | FETCH-LOGICAL-c404t-86b758cf5a2946da49801313a6251c896e95d716ec7066b3ddbb67db33f55e8f3 |
| IEDL.DBID | UNPAY |
| ISSN | 2193-567X 1319-8025 2191-4281 |
| IngestDate | Sun Oct 26 02:54:30 EDT 2025 Tue Sep 30 16:36:33 EDT 2025 Mon Jun 30 09:08:21 EDT 2025 Thu Jan 02 22:55:01 EST 2025 Wed Oct 01 06:35:11 EDT 2025 Thu Apr 24 23:07:03 EDT 2025 Mon Jul 21 06:07:01 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | Parallel corpus Python code Statistical machine translation Pseudo-code Neural machine translation |
| Language | English |
| License | King Fahd University of Petroleum & Minerals 2022. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c404t-86b758cf5a2946da49801313a6251c896e95d716ec7066b3ddbb67db33f55e8f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Report-3 ObjectType-Case Study-4 |
| ORCID | 0000-0002-2590-1097 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://link.springer.com/content/pdf/10.1007/s13369-022-07049-0.pdf |
| PMID | 35874184 |
| PQID | 2774560127 |
| PQPubID | 2044268 |
| PageCount | 14 |
| ParticipantIDs | unpaywall_primary_10_1007_s13369_022_07049_0 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9296120 proquest_journals_2774560127 pubmed_primary_35874184 crossref_citationtrail_10_1007_s13369_022_07049_0 crossref_primary_10_1007_s13369_022_07049_0 springer_journals_10_1007_s13369_022_07049_0 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-02-01 |
| PublicationDateYYYYMMDD | 2023-02-01 |
| PublicationDate_xml | – month: 02 year: 2023 text: 2023-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Germany – name: Heidelberg |
| PublicationTitle | Arabian journal for science and engineering |
| PublicationTitleAbbrev | Arab J Sci Eng |
| PublicationTitleAlternate | Arab J Sci Eng |
| PublicationYear | 2023 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | 7049_CR31 7049_CR10 7049_CR32 7049_CR7 7049_CR6 7049_CR5 7049_CR3 7049_CR2 7049_CR1 7049_CR19 7049_CR17 7049_CR39 7049_CR18 7049_CR15 7049_CR37 RJ Williams (7049_CR38) 1989; 1 7049_CR13 7049_CR14 7049_CR36 7049_CR9 7049_CR11 7049_CR33 7049_CR8 7049_CR12 7049_CR34 7049_CR20 7049_CR21 H Voormann (7049_CR35) 2008; 4 7049_CR40 A Lopez (7049_CR25) 2008; 40 P Koehn (7049_CR22) 2009 S Hochreiter (7049_CR16) 1997; 9 7049_CR28 7049_CR29 7049_CR26 7049_CR27 7049_CR24 J An (7049_CR4) 2020; 7 FJ Pineda (7049_CR30) 1988; 4 7049_CR23 |
| References_xml | – ident: 7049_CR14 – volume: 4 start-page: 235 issue: 2 year: 2008 ident: 7049_CR35 publication-title: Corpus Linguist. Linguist. Theory doi: 10.1515/CLLT.2008.010 – ident: 7049_CR8 doi: 10.1145/3468264.3468611 – ident: 7049_CR9 doi: 10.3115/v1/W14-4012 – ident: 7049_CR12 – ident: 7049_CR39 doi: 10.1109/APSEC.2018.00101 – ident: 7049_CR15 doi: 10.21236/ADA460212 – ident: 7049_CR26 – ident: 7049_CR3 – volume: 1 start-page: 270 issue: 2 year: 1989 ident: 7049_CR38 publication-title: Neural Comput. doi: 10.1162/neco.1989.1.2.270 – ident: 7049_CR1 doi: 10.1109/ASWEC.2018.00011 – ident: 7049_CR20 doi: 10.18653/v1/D13-1176 – ident: 7049_CR28 doi: 10.1109/ase.2015.36 – ident: 7049_CR34 – ident: 7049_CR7 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 7049_CR16 publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – ident: 7049_CR17 – volume-title: Statistical Machine Translation year: 2009 ident: 7049_CR22 doi: 10.1017/CBO9780511815829 – ident: 7049_CR31 doi: 10.1145/3340531.3412048 – ident: 7049_CR13 – ident: 7049_CR40 – volume: 40 start-page: 8 issue: 3 year: 2008 ident: 7049_CR25 publication-title: ACM Comput. Surv. (CSUR) doi: 10.1145/1380584.1380586 – ident: 7049_CR36 doi: 10.1109/SERVICES-I.2009.56 – ident: 7049_CR11 – ident: 7049_CR24 doi: 10.1109/ICSE.2019.00087 – ident: 7049_CR5 doi: 10.1145/2487575.2487600 – ident: 7049_CR10 doi: 10.3115/v1/D14-1179 – ident: 7049_CR2 – ident: 7049_CR27 – ident: 7049_CR19 doi: 10.1007/978-3-319-18032-8_20 – volume: 4 start-page: 216 issue: 3 year: 1988 ident: 7049_CR30 publication-title: J. Complex. doi: 10.1016/0885-064X(88)90021-0 – ident: 7049_CR33 – ident: 7049_CR29 doi: 10.3115/1073083.1073135 – volume: 7 start-page: 84 issue: 1 year: 2020 ident: 7049_CR4 publication-title: IEEE Trans. Comput. Soc. Syst. doi: 10.1109/TCSS.2019.2956481 – ident: 7049_CR6 – ident: 7049_CR21 doi: 10.4324/9780203856949-35 – ident: 7049_CR32 doi: 10.1145/3488560.3502182 – ident: 7049_CR18 – ident: 7049_CR37 – ident: 7049_CR23 doi: 10.18653/v1/W17-3204 |
| SSID | ssib048395113 ssj0001916267 ssj0061873 |
| Score | 2.288574 |
| Snippet | The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability... |
| SourceID | unpaywall pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1845 |
| SubjectTerms | Algorithms Availability Computer Engineering and Computer Science Engineering Humanities and Social Sciences Machine learning Machine translation multidisciplinary Research Article-Computer Engineering and Computer Science Science |
| Title | Is the Corpus Ready for Machine Translation? A Case Study with Python to Pseudo-Code Corpus |
| URI | https://link.springer.com/article/10.1007/s13369-022-07049-0 https://www.ncbi.nlm.nih.gov/pubmed/35874184 https://www.proquest.com/docview/2774560127 https://pubmed.ncbi.nlm.nih.gov/PMC9296120 https://link.springer.com/content/pdf/10.1007/s13369-022-07049-0.pdf |
| UnpaywallVersion | publishedVersion |
| Volume | 48 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 2191-4281 dateEnd: 20241101 omitProxy: true ssIdentifier: ssj0001916267 issn: 2191-4281 databaseCode: ABDBF dateStart: 20041001 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 2191-4281 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0061873 issn: 2193-567X databaseCode: GX1 dateStart: 20020101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 2191-4281 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001916267 issn: 2191-4281 databaseCode: AFBBN dateStart: 20110101 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 2191-4281 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0061873 issn: 2193-567X databaseCode: AGYKE dateStart: 20110101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 2191-4281 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0061873 issn: 2193-567X databaseCode: U2A dateStart: 20110101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Jj9MwFLZm2gPMgUUsUxgqH7gx7jTxEvuEOh3KgNSqByoVcYjiJQJRpRVNhMqv5zlbKYNGIC6RpTje8ix_z_78PYRemsgHtLUB4TyNCNNuSBRVmhgllbERs9r4Df3pTFwv2PslXx6hq-YuTMl2b44kqzsNXqUpyy82Nr3YX3yjVCjimehgsgxSA3h9jLqCAyLvoO5iNh999HHlwB0hgLCDKk0JF9Gyvjvz54IO16cboPMmd7I9QD1Bd4psk-y-J6vVL2vU5D5yTe8qasrXQZHrgfnxm_Dj_3b_AbpXg1g8qqzuITpy2SP06d0WA5rEXhu52GLPzt9hgMR4WvI1HS6XxYp69xqP8BjWT-xpjDvsN4PxfOdlDHC-xvOtK-yajNe2Ke0xWkzefBhfkzp0AzFsyHIihQZHxKQ8CRUTNmFKemEfmoC7FRiphFPcgqvmTASYR1NrtRaR1ZSmnDuZ0ieok60zd4pwmirBNXdBSiUzUibSMh0mVEijreash4Lmh8Wm1jX34TVW8V6R2Q9XDMMVl8MVD3voVfvNplL1uDX3WWMHcT3Dt3EIuNl7s2HUQ08rk2iLolx6USBoWnRgLG0Gr-l9-Cb78rnU9ga0CpgTqjxvrGBf5W0tPG9N7y869Ozfsj9Hd0OAdhVX_Qx18m-FewFQLNd91B1dXl1O-uj47TKA52w-7dez7yeonCtH |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JbxMxFH4q6QE4sIgtUJAP3KjTzHgZ-4SiQFWQWuVApCAOo_EyAhFNomZGVfj1PM8WQlEF4mbJHm_zLH_P_t5ngNc2CQ_auogKkSeUGz-mmmlDrVbauoQ7Y8OB_vmFPJvzjwuxOIB3XSxMzXbvriSbmIag0lSUJ2uXn-wC3xiTmgYmOposx9QIs2_BoRSIyAdwOL-YTT6Hd-XQHaGIsKMmzaiQyaKNnflzRfv70zXQeZ072V-g3oXbVbHOtlfZcvnLHnV6H3w3uoaa8n1UlWZkf_wm_Pi_w38A91oQSyaN1T2EA188gi8fNgTRJAnayNWGBHb-liAkJuc1X9OTeltsqHdvyYRMcf8kgca4JeEwmMy2QcaAlCsy2_jKreh05braHsP89P2n6Rltn26glo95SZU06IjYXGSx5tJlXKsg7MMydLciq7T0Wjh01bxNEPMY5pwxMnGGsVwIr3L2BAbFqvDPgOS5lsIIH-VMcatUphw3ccakssYZwYcQdT8sta2ueXheY5nuFJnDdKU4XWk9Xel4CG_6b9aNqseNpY86O0jbFb5JY8TNwZuNkyE8bUyir4oJFUSBsGvJnrH0BYKm935O8e1rre2NaBUxJzZ53FnBrsmbenjcm95fDOj5vxV_AXdihHYNV_0IBuVl5V8iFCvNq3al_QRw2yfY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Is+the+Corpus+Ready+for+Machine+Translation%3F+A+Case+Study+with+Python+to+Pseudo-Code+Corpus&rft.jtitle=The+Arabian+Journal+for+Science+and+Engineering.+Section+B%2C+Engineering&rft.date=2023-02-01&rft.pub=Springer+Nature+B.V&rft.issn=1319-8025&rft.eissn=2191-4281&rft.volume=48&rft.issue=2&rft.spage=1845&rft.epage=1858&rft_id=info:doi/10.1007%2Fs13369-022-07049-0&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2193-567X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2193-567X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2193-567X&client=summon |