The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message formalized as a set of entity-relationship triples. It embeds the LM in an auto-encoder by feeding its output to a semantic parser whose output is in the same representation domain as the input message. Compared to a baseline that generates text using greedy search, we demonstrate two techniques that improve the fluency and semantic accuracy of the generated text: The first technique samples multiple candidate text sequences from which the semantic parser chooses. The second trains the language model while keeping the semantic parser frozen to improve the semantic accuracy of the auto-encoder. We carry out experiments on the English WebNLG 3.0 data set, using BLEU to measure the fluency of generated text and standard parsing metrics to measure semantic accuracy. We show that our proposed approaches significantly improve on the greedy search baseline. Human evaluation corroborates the results of the automatic evaluation experiments.

We address a spatial conservation planning problem in which the planner purchases a budget-constrained set of land parcels in order to maximize the expected spread of a population of an endangered species. Existing techniques based on the sample average approximation scheme and standard integer programming methods have high complexity and limited scalability. We propose a fast combinatorial optimization algorithm using Lagrangian relaxation and primal-dual techniques to solve the problem approximately. The algorithm provides a new way to address a range of conservation planning and scheduling problems. On the Red-cockaded Woodpecker data, our algorithm produces near optimal solutions and runs significantly faster than a standard mixed integer program solver. Compared with a greedy baseline, the solution quality is comparable or better, but our algorithm is 10–30 times faster. On synthetic problems that do not exhibit submodularity, our algorithm significantly outperforms the greedy baseline.