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This work finds limited evidence supporting the theory that using multipletasks with sequence-to-sequence transformer language models can improveperformance on some metrics. In particular, the multi-task generalist t5-smalloutperforms the specialist t5-small with a $F_1$ of $0.771$ up from $0.692$,which may point to underlying cross-task knowledge generalization. This furthersuggests that even with the same network, "re-using" the same data in adifferent way may lead to higher performance in some metrics. However, theinverse task alone is likely only an optimization strategy, since it does notyield a significant general improvement at the model sizes explored in thiswork. Also, adding $\approx 4500$ LLM annotated records (interlaced with the$12800$ WebNLG training records) does not substantially change automatic metricperformance compared to the same t5-small model without the synthetic data.This may be due to a learning capacity bottleneck on account of model size, anddecreases observed may be due to distributional differences in the corpora.Future research using larger models or human evaluation is required to morefully explain the mechanisms contributing to performance on these tasks.

Inroads to a Structured Data Natural Language Bijection and the role of LLM annotation