{"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/improving-neural-machine-translation-with","title":"Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets","arxiv_id":"1703.04887","date":"2017-03-15","proceeding":"NAACL 2018 6","authors":["Zhen Yang","Wei Chen","Feng Wang","Bo Xu"],"abstract":"This paper proposes an approach for applying GANs to NMT. We build a\nconditional sequence generative adversarial net which comprises of two\nadversarial sub models, a generator and a discriminator. The generator aims to\ngenerate sentences which are hard to be discriminated from human-translated\nsentences (i.e., the golden target sentences), And the discriminator makes\nefforts to discriminate the machine-generated sentences from human-translated\nones. The two sub models play a mini-max game and achieve the win-win situation\nwhen they reach a Nash Equilibrium. Additionally, the static sentence-level\nBLEU is utilized as the reinforced objective for the generator, which biases\nthe generation towards high BLEU points. During training, both the dynamic\ndiscriminator and the static BLEU objective are employed to evaluate the\ngenerated sentences and feedback the evaluations to guide the learning of the\ngenerator. Experimental results show that the proposed model consistently\noutperforms the traditional RNNSearch and the newly emerged state-of-the-art\nTransformer on English-German and Chinese-English translation tasks.","url_abs":"http://arxiv.org/abs/1703.04887v4","url_pdf":"http://arxiv.org/pdf/1703.04887v4.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":"improving-neural-machine-translation-with","repo_url":"https://github.com/ZhenYangIACAS/NMT_GAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"improving-neural-machine-translation-with","repo_url":"https://github.com/qiuguoxia/chatbotmodal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"improving-neural-machine-translation-with","repo_url":"https://github.com/yukio326/gan-nmt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"nmt","task_name":"NMT"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.04887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.04887"}},"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. 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