{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/edinburgh-neural-machine-translation-systems","title":"Edinburgh Neural Machine Translation Systems for WMT 16","arxiv_id":"1606.02891","date":"2016-06-09","proceeding":"WS 2016 8","authors":["Rico Sennrich","Barry Haddow","Alexandra Birch"],"abstract":"We participated in the WMT 2016 shared news translation task by building\nneural translation systems for four language pairs, each trained in both\ndirections: English<->Czech, English<->German, English<->Romanian and\nEnglish<->Russian. Our systems are based on an attentional encoder-decoder,\nusing BPE subword segmentation for open-vocabulary translation with a fixed\nvocabulary. We experimented with using automatic back-translations of the\nmonolingual News corpus as additional training data, pervasive dropout, and\ntarget-bidirectional models. All reported methods give substantial\nimprovements, and we see improvements of 4.3--11.2 BLEU over our baseline\nsystems. In the human evaluation, our systems were the (tied) best constrained\nsystem for 7 out of 8 translation directions in which we participated.","url_abs":"http://arxiv.org/abs/1606.02891v2","url_pdf":"http://arxiv.org/pdf/1606.02891v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"edinburgh-neural-machine-translation-systems","repo_url":"https://github.com/rsennrich/wmt16-scripts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"bpe","method_name":"BPE"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/machine-translation-on-wmt2016-czech-english","task":"Machine Translation","dataset":"WMT2016 Czech-English","model":"Attentional encoder-decoder + BPE","rank_in_archive_order":1,"of":1,"metrics":{"BLEU score":"31.4"},"uses_additional_data":false},{"leaderboard":"/sota/machine-translation-on-wmt2016-english-czech","task":"Machine Translation","dataset":"WMT2016 English-Czech","model":"Attentional encoder-decoder + BPE","rank_in_archive_order":1,"of":1,"metrics":{"BLEU score":"25.8"},"uses_additional_data":false},{"leaderboard":"/sota/machine-translation-on-wmt2016-english-german","task":"Machine Translation","dataset":"WMT2016 English-German","model":"Attentional encoder-decoder + BPE","rank_in_archive_order":2,"of":12,"metrics":{"BLEU score":"34.2"},"uses_additional_data":false},{"leaderboard":"/sota/machine-translation-on-wmt2016-english-1","task":"Machine Translation","dataset":"WMT2016 English-Romanian","model":"BiGRU","rank_in_archive_order":13,"of":21,"metrics":{"BLEU score":"28.1"},"uses_additional_data":false},{"leaderboard":"/sota/machine-translation-on-wmt2016-english","task":"Machine Translation","dataset":"WMT2016 English-Russian","model":"Attentional encoder-decoder + BPE","rank_in_archive_order":1,"of":4,"metrics":{"BLEU score":"26.0"},"uses_additional_data":false},{"leaderboard":"/sota/machine-translation-on-wmt2016-german-english","task":"Machine Translation","dataset":"WMT2016 German-English","model":"Attentional encoder-decoder + BPE","rank_in_archive_order":3,"of":8,"metrics":{"BLEU score":"38.6"},"uses_additional_data":false},{"leaderboard":"/sota/machine-translation-on-wmt2016-romanian","task":"Machine Translation","dataset":"WMT2016 Romanian-English","model":"Attentional encoder-decoder + BPE","rank_in_archive_order":6,"of":21,"metrics":{"BLEU score":"33.3"},"uses_additional_data":false},{"leaderboard":"/sota/machine-translation-on-wmt2016-russian","task":"Machine Translation","dataset":"WMT2016 Russian-English","model":"Attentional encoder-decoder + BPE","rank_in_archive_order":1,"of":1,"metrics":{"BLEU score":"28.0"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1606.02891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.02891"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rsennrich/wmt16-scripts","reach":null}],"summary":{"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"211886d8e54417ed","entry":"escape_special_chars","repo":"rsennrich/wmt16-scripts","repo_kind":"official","path":"preprocess/conll_to_factors.py","file_url":"https://github.com/rsennrich/wmt16-scripts/blob/HEAD/preprocess/conll_to_factors.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"211886d8e54417ed"}},{"code_sha256_prefix":"5d2fe64366cd6ab0","entry":"get_factors","repo":"rsennrich/wmt16-scripts","repo_kind":"official","path":"preprocess/conll_to_factors.py","file_url":"https://github.com/rsennrich/wmt16-scripts/blob/HEAD/preprocess/conll_to_factors.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5d2fe64366cd6ab0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}