In this paper, they tried a couple different methods for determining how similar the incoming request is to requests that they have scored in their dataset. Act

In this paper, they tried a couple different methods for determining how similar the incoming request is to requests that they have scored in their dataset. Actually, one of the best methods they used does not involve using a model at all to evaluate the incoming query (similarity-weighted ranking) although it _does_ use pre-trained embeddings. Using this, they were able to produce quite good results applying this similarity measurement to unseen queries using a standard benchmark. The leap of faith here is assuming that the same query similarity method will continue to bear fruit when extended to queries that aren't benchmarkable.