Improving adversarial text generation by modeling the distant future
Publication
, Conference
Zhang, R; Chen, C; Gan, Z; Wang, W; Shen, D; Wang, G; Wen, Z; Carin, L
Published in: Proceedings of the Annual Meeting of the Association for Computational Linguistics
January 1, 2020
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments demonstrate that the proposed method leads to improved performance.
Duke Scholars
Published In
Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN
0736-587X
ISBN
9781952148255
Publication Date
January 1, 2020
Start / End Page
2516 / 2531
Citation
APA
Chicago
ICMJE
MLA
NLM
Zhang, R., Chen, C., Gan, Z., Wang, W., Shen, D., Wang, G., … Carin, L. (2020). Improving adversarial text generation by modeling the distant future. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 2516–2531).
Zhang, R., C. Chen, Z. Gan, W. Wang, D. Shen, G. Wang, Z. Wen, and L. Carin. “Improving adversarial text generation by modeling the distant future.” In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2516–31, 2020.
Zhang R, Chen C, Gan Z, Wang W, Shen D, Wang G, et al. Improving adversarial text generation by modeling the distant future. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2020. p. 2516–31.
Zhang, R., et al. “Improving adversarial text generation by modeling the distant future.” Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2020, pp. 2516–31.
Zhang R, Chen C, Gan Z, Wang W, Shen D, Wang G, Wen Z, Carin L. Improving adversarial text generation by modeling the distant future. Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2020. p. 2516–2531.
Published In
Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN
0736-587X
ISBN
9781952148255
Publication Date
January 1, 2020
Start / End Page
2516 / 2531