EAT2seq: A generic framework for controlled sentence transformation without task-specific training
Table 1: Boolean grammatical features in the EAT-vector format (RP = relative pronoun)
['[BOLD] Feature class', '[BOLD] Index', '[BOLD] Feature meaning']
3.2.4 Vector format We transform each EAT-tuple in the sequence into a vector using Boolean encoding for grammatical and negation features, and pre-trained embeddings to represent words. The length of each vector is thus 28+3S, where S is the embedding length. The first 28 Boolean components specify grammatical features, and the rest is derived by concatenating the embeddings of the Event, Agent, and Theme, in this order. The empty token \varnothing corresponds to the zero embedding. We begin the EAT-sequence of each sentence with the thematic specification of the main clause, the root verb occupying the Event position. The beam search ended if the maximum sentence length (20) was achieved, or if all candidates contained a sentence-final token. This could be a dedicated end-of-sentence token, or a sentence-final punctuation marker (.!?). Instead of directly using the most likely final candidate, we made EATs from all k final candidates and compared them with the original EAT. To ensure their proper retainment, we punished their deviation from those in the original EAT with 10. In other features we applied a reward or punishment of 1 for identity or divergence. The rationale was to strictly enforce the grammatical class of the transformation, but be more lenient in allowing lexical paraphrases. The candidate with the highest probability after the EAT-comparison was chosen as the final transformation.