{"_buckets": {"deposit": "8e1ce4f7-06e1-4030-8d98-de299c90c70e"}, "_deposit": {"created_by": 73, "id": "7669", "owner": "73", "owners": [73], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "7669"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00007669", "sets": ["user-miit"]}, "communities": ["miit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Neural Machine Translation between Myanmar Sign Language and Myanmar Written Text", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "We explore Neural Machine Translation (NMT) between Myanmar Sign Language (MSL) and Myanmar Written Text (MWT). Our developing MSL-MWT parallel corpus was used and the experiments were carried\nout using three different NMT approaches: Recurrent Neural Network (RNN), Trasformer, and the Convolutional Neural Network (CNN). In addition, four different segmentation schemes for word embedding\nwere studies, these were syllable segmentation, word segmentation (sign unit based word segmentation for MSL), SentencePiece and the Byte-Pair-Encoding (BPE). The results show that the highest quality NMT and Statistical Machine Translation (SMT) performances\nwere attained with syllable segmentation for both MSL and MWT. We\nfound that Transformer outperformed both CNN and RNN for MWT-to-MSL and MSLto-MWT translation tasks."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Neural Machine Translation (NMT), Myanmar Sign Language (MSL), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Byte-Pair- Encoding (BPE)"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2021-01-20"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Neural Machine Translation between Myanmar Sign Language and Myanmar Written Text.pdf", "filesize": [{"value": "323 KB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_3", "mimetype": "application/pdf", "size": 323000.0, "url": {"url": "https://meral.edu.mm/record/7669/files/Neural Machine Translation between Myanmar Sign Language and Myanmar Written Text.pdf"}, "version_id": "74cd9a2a-e736-4d18-b050-3c3ceb4eaf4a"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_journal_title": "Neural Machine Translation between Myanmar Sign Language and Myanmar Written Text", "subitem_volume": "In the second Regional Conference on Optical character recognition and Natural language processing technologies for ASEAN languages 2018 (ONA 2018)"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Swe Zin Moe"}, {"subitem_authors_fullname": "Ye Kyaw Thu"}, {"subitem_authors_fullname": "Hnin Aye Thant"}, {"subitem_authors_fullname": "Nandar Win Min"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Journal article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2018-12-18"}, "item_title": "Neural Machine Translation between Myanmar Sign Language and Myanmar Written Text", "item_type_id": "21", "owner": "73", "path": ["1582963674932", "1597396989070"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000007669", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2018-12-18"}, "publish_date": "2018-12-18", "publish_status": "0", "recid": "7669", "relation": {}, "relation_version_is_last": true, "title": ["Neural Machine Translation between Myanmar Sign Language and Myanmar Written Text"], "weko_shared_id": -1}