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This work studies the task of glossification, of which the aim is to emtranscribe natural spoken language sentences for the Deaf (hard-of-hearing)community to ordered sign language glosses. Previous sequence-to-sequencelanguage models trained with paired sentence-gloss data often fail to capturethe rich connections between the two distinct languages, leading tounsatisfactory transcriptions. We observe that despite different grammars,glosses effectively simplify sentences for the ease of deaf communication,while sharing a large portion of vocabulary with sentences. This has motivatedus to implement glossification by executing a collection of editing actions,e.g. word addition, deletion, and copying, called editing programs, on theirnatural spoken language counterparts. Specifically, we design a new neuralagent that learns to synthesize and execute editing programs, conditioned onsentence contexts and partial editing results. The agent is trained to imitateminimal editing programs, while exploring more widely the program space viapolicy gradients to optimize sequence-wise transcription quality. Results showthat our approach outperforms previous glossification models by a large margin.

Transcribing Natural Languages for The Deaf via Neural Editing Programs