We conduct a large-scale, systematic study to evaluate the existing evaluation methods
for natural language generation in the context of generating online product reviews. We
compare human-based evaluators with a variety of automated evaluation procedures, including discriminative evaluators that measure how well machine-generated text can be distinguished from human-written text, as well as word overlap metrics that assess how similar the generated text compares to human-written references. We determine to what extent these different evaluators agree on the ranking of a dozen of state-of-the-art generators for online product reviews. We find that human evaluators do not correlate well with discriminative evaluators, leaving a bigger question of whether adversarial accuracy is the correct objective for natural language generation. In general, distinguishing machine-generated text is challenging even for human evaluators, and human decisions correlate better with lexical overlaps. We find lexical diversity an intriguing metric that is indicative of the assessments of different evaluators. A post-experiment survey of participants provides insights into how to evaluate and improve the quality of natural language generation systems.
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Hierarchical Text Generation using an Outline
Many challenges in natural language pro- cessing require generating text, including language translation, dialogue generation, and speech recognition. For all of these problems, text generation becomes more difficult as the text becomes longer. Cur- rent language models often struggle to keep track of coherence for long pieces of text. Here, we attempt to have the model construct and use an outline of the text it generates to keep it focused. We find that the usage of an outline improves perplex- ity. We do not find that using the outline improves human evaluation over a simpler baseline, revealing a discrepancy in per- plexity and human perception. Similarly, hierarchical generation is not found to im- prove human evaluation scores.
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- Award ID(s):
- 1659788
- PAR ID:
- 10098861
- Date Published:
- Journal Name:
- International Conference on Natural Language Processing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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