{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T18:34:38Z","timestamp":1763058878037,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11208-1","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T15:43:34Z","timestamp":1680536614000},"page":"9435-9466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["LenM: Improving Low-Resource Neural Machine Translation Using Target Length Modeling"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0860-367X","authenticated-orcid":false,"given":"Mohammad Mahdi","family":"Mahsuli","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5499-6542","authenticated-orcid":false,"given":"Shahram","family":"Khadivi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2379-4427","authenticated-orcid":false,"given":"Mohammad Mehdi","family":"Homayounpour","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"11208_CR1","unstructured":"Kalchbrenner N, Blunsom P (2013) Recurrent continuous translation models. In: Proceedings of the 2013 conference on empirical methods in natural language processing (EMNLP 2013), pp 1700\u20131709"},{"key":"11208_CR2","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In Advances in neural information processing systems (NIPS), pp 3104\u20133112"},{"key":"11208_CR3","doi-asserted-by":"crossref","unstructured":"Cho K, Merrienboer B, Gulcehre C, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"11208_CR4","unstructured":"Bahdanau D, Cho KH, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: 3rd international conference on learning representations (ICLR 2015)"},{"key":"11208_CR5","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"issue":"3","key":"11208_CR6","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1162\/coli_a_00446","volume":"48","author":"B Haddow","year":"2022","unstructured":"Haddow B, Bawden R, Barone AVM, Helcl J, Birch A (2022) Survey of low-resource machine translation. Comput Linguist (COLING) 48(3):673\u2013732","journal-title":"Comput Linguist (COLING)"},{"key":"11208_CR7","doi-asserted-by":"crossref","unstructured":"Koehn P, Knowles R (2017) Six challenges for neural machine translation. In: Proceedings of the first workshop on neural machine translation, pp 28\u201339","DOI":"10.18653\/v1\/W17-3204"},{"key":"11208_CR8","doi-asserted-by":"crossref","unstructured":"Stahlberg F, Byrne B (2019) On NMT search errors and model errors: cat got your tongue? In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 3356\u20133362","DOI":"10.18653\/v1\/D19-1331"},{"key":"11208_CR9","doi-asserted-by":"crossref","unstructured":"Shaw P, Uszkoreit J, Vaswani A (2018) Self-attention with relative position representations. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 2 (short papers), pp 464\u2013468","DOI":"10.18653\/v1\/N18-2074"},{"key":"11208_CR10","unstructured":"Gu J, Bradbury J, Xiong C, Li VO, Socher R (2018) Non-autoregressive neural machine translation. In: International conference on learning representations (ICLR)"},{"key":"11208_CR11","doi-asserted-by":"crossref","unstructured":"Lee J, Mansimov E, Cho K (2018) Deterministic non-autoregressive neural sequence modeling by iterative refinement. In: 2018 Conference on Empirical methods in natural language processing (EMNLP 2018), Association for Computational Linguistics (ACL), pp 1173\u20131182","DOI":"10.18653\/v1\/D18-1149"},{"key":"11208_CR12","doi-asserted-by":"crossref","unstructured":"Ghazvininejad M, Levy O, Liu Y, Zettlemoyer L (2019) Mask-predict: parallel decoding of conditional masked language models. In: Proceedings of the 2019 Conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 6112\u20136121","DOI":"10.18653\/v1\/D19-1633"},{"key":"11208_CR13","doi-asserted-by":"crossref","unstructured":"Murray K, Chiang D (2018) Correcting length bias in neural machine translation. In: Proceedings of the third conference on machine translation: research papers, pp 212\u2013223","DOI":"10.18653\/v1\/W18-6322"},{"key":"11208_CR14","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18:602\u2013610","journal-title":"Neural Netw"},{"key":"11208_CR15","doi-asserted-by":"crossref","unstructured":"Ray A, Rajeswar S, Chaudhury S (2015) Text recognition using deep BLSTM networks. In: 2015 Eighth international conference on advances in pattern recognition (ICAPR)","DOI":"10.1109\/ICAPR.2015.7050699"},{"key":"11208_CR16","doi-asserted-by":"crossref","unstructured":"Lefebvre G, Berlemont S, Mamalet F, Garcia C (2013) BLSTM-RNN based 3D gesture classification. In: International conference on artificial neural networks","DOI":"10.1007\/978-3-642-40728-4_48"},{"key":"11208_CR17","doi-asserted-by":"crossref","unstructured":"Fu SW, Tsao Y, Hwang HT, Wang HM (2018) Quality-net: an end-to-end non-intrusive speech quality assessment model based on BLSTM. arXiv:1808.05344","DOI":"10.21437\/Interspeech.2018-1802"},{"key":"11208_CR18","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45:2673\u20132681","journal-title":"IEEE Trans Signal Process"},{"key":"11208_CR19","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780","journal-title":"Neural Comput"},{"key":"11208_CR20","unstructured":"Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K (2016) Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144"},{"key":"11208_CR21","unstructured":"Graves A (2013) Generating sequences with recurrent neural networks. arXiv:1308.0850"},{"key":"11208_CR22","unstructured":"Graves A, Wayne G, Danihelka I (2014) Neural turing machines. arXiv:1410.5401"},{"key":"11208_CR23","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"11208_CR24","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: 2019 Conference of the North American chapter of the Association for Computational Linguistics\u2014human language technologies (NAACL-HLT 2019) NAACL-HLT (1)"},{"key":"11208_CR25","doi-asserted-by":"crossref","unstructured":"Jean S, Firat O, Cho K, Memisevic R, Bengio Y (2015) Montreal neural machine translation systems for WMT\u201915. In: Proceedings of the tenth workshop on statistical machine translation, pp 134\u2013140","DOI":"10.18653\/v1\/W15-3014"},{"key":"11208_CR26","unstructured":"Boulanger-Lewandowski N, Bengio Y, Vincent P (2013) Audio chord recognition with recurrent neural networks. In: 14th International society for music information retrieval conference (ISMIR 2013), pp 335\u2013340"},{"key":"11208_CR27","doi-asserted-by":"crossref","unstructured":"He W, He Z, Wu H, Wang H (2016) Improved neural machine translation with SMT features. In: Thirtieth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.9983"},{"key":"11208_CR28","doi-asserted-by":"crossref","unstructured":"Wu C, Wu F, Huang Y (2021) Da-transformer: distance-aware transformer. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies (NAACL-HLT 2021), pp 2059\u20132068","DOI":"10.18653\/v1\/2021.naacl-main.166"},{"issue":"3","key":"11208_CR29","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1162\/coli_a_00445","volume":"48","author":"P Dufter","year":"2022","unstructured":"Dufter P, Schmitt M, Sch\u00fctze H (2022) Position information in transformers: an overview. Comput Linguist (COLING) 48(3):733\u2013763","journal-title":"Comput Linguist (COLING)"},{"key":"11208_CR30","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu W-J (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"11208_CR31","unstructured":"Klein G, Hernandez F, Nguyen V, Senellart J (2020) The OpenNMT neural machine translation toolkit: 2020 edition. In: Proceedings of the 14th conference of the association for machine translation in the Americas (AMTA), (volume 1: research track)"},{"key":"11208_CR32","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L (2019) Pytorch: an imperative style, high-performance deep learning library. In: Advances in neural information processing systems (NIPS), vol 32"},{"key":"11208_CR33","unstructured":"Cettolo M, Girardi C, Federico M (2012) Wit3: web inventory of transcribed and translated talks. In: Conference of European association for machine translation, pp 261\u2013268"},{"key":"11208_CR34","unstructured":"Cettolo M, Jan N, Sebastian S, Bentivogli L, Cattoni R, Federico M (2016) The IWSLT 2016 evaluation campaign. In: International Workshop on spoken language translation (IWSLT)"},{"key":"11208_CR35","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv:1607.06450"},{"key":"11208_CR36","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR workshop and conference proceedings, pp 249\u2013256"},{"key":"11208_CR37","unstructured":"Rosendahl J, Tran VAK, Wang W, Ney H (2019) Analysis of positional encodings for neural machine translation. In: Proceedings of the 16th international workshop on spoken language translation (IWSLT 2019), Hong Kong, China"},{"key":"11208_CR38","unstructured":"Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th conference of the association for machine translation in the Americas: technical papers, pp 223\u2013231"},{"key":"11208_CR39","doi-asserted-by":"crossref","unstructured":"Popovi\u0107 M (2015) chrF: character n-gram F-score for automatic MT evaluation. In: Proceedings of the tenth workshop on statistical machine translation, pp 392\u2013395","DOI":"10.18653\/v1\/W15-3049"},{"key":"11208_CR40","doi-asserted-by":"crossref","unstructured":"Popovi\u0107 M (2016) chrF deconstructed: beta parameters and n-gram weights. In: Proceedings of the first conference on machine translation: volume 2, shared task papers, pp 499\u2013504","DOI":"10.18653\/v1\/W16-2341"},{"key":"11208_CR41","doi-asserted-by":"crossref","unstructured":"Rei R, Stewart C, Farinha AC, Lavie A (2020) COMET: a neural framework for MT evaluation. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP 2020), pp 2685\u20132702","DOI":"10.18653\/v1\/2020.emnlp-main.213"},{"key":"11208_CR42","first-page":"7059","volume":"32","author":"A Conneau","year":"2019","unstructured":"Conneau A, Lample G (2019) Cross-lingual language model pretraining. Adv Neural Inf Process Syst (NIPS) 32:7059\u20137069","journal-title":"Adv Neural Inf Process Syst (NIPS)"},{"key":"11208_CR43","doi-asserted-by":"crossref","unstructured":"Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzm\u00e1n F, Grave \u00c9, Ott M, Zettlemoyer L, Stoyanov V (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th annual meeting of the Association for Computational Linguistics, pp 8440\u20138451","DOI":"10.18653\/v1\/2020.acl-main.747"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11208-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11208-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11208-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T17:17:34Z","timestamp":1699723054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11208-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,31]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11208"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11208-1","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2023,3,31]]},"assertion":[{"value":"24 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}