Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast

Vrindavan Harrison, Lena Reed, Shereen Oraby, Marilyn Walker


Abstract
Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of data describing a restaurant. While earlier work focused solely on the semantic fidelity of outputs, recent work has started to explore methods for controlling the style of the generated text while simultaneously achieving semantic accuracy. Here we experiment with two stylistic benchmark tasks, generating language that exhibits variation in personality, and generating discourse contrast. We report a huge performance improvement in both stylistic control and semantic accuracy over the state of the art on both of these benchmarks. We test several different models and show that putting stylistic conditioning in the decoder and eliminating the semantic re-ranker used in earlier models results in more than 15 points higher BLEU for Personality, with a reduction of semantic error to near zero. We also report an improvement from .75 to .81 in controlling contrast and a reduction in semantic error from 16% to 2%.
Anthology ID:
W19-8101
Volume:
Proceedings of the 1st Workshop on Discourse Structure in Neural NLG
Month:
November
Year:
2019
Address:
Tokyo, Japan
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–12
Language:
URL:
https://aclanthology.org/W19-8101
DOI:
10.18653/v1/W19-8101
Bibkey:
Cite (ACL):
Vrindavan Harrison, Lena Reed, Shereen Oraby, and Marilyn Walker. 2019. Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast. In Proceedings of the 1st Workshop on Discourse Structure in Neural NLG, pages 1–12, Tokyo, Japan. Association for Computational Linguistics.
Cite (Informal):
Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast (Harrison et al., 2019)
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PDF:
https://preview.aclanthology.org/update-css-js/W19-8101.pdf