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The sliding window approach provides an elegant way to handle contexts ofsizes larger than the Transformer's input window, for tasks like languagemodeling. Here we extend this approach to the sequence-to-sequence task ofdocument parsing. For this, we exploit recent progress in transition-basedparsing to implement a parser with synchronous sliding windows over source andtarget. We develop an oracle and a parser for document-level AMR by expandingon Structured-BART such that it leverages source-target alignments andconstrains decoding to guarantee synchronicity and consistency acrossoverlapping windows. We evaluate our oracle and parser using the AbstractMeaning Representation (AMR) parsing 3.0 corpus. On the Multi-Sentencedevelopment set of AMR 3.0, we show that our transition oracle loses only 8\%of the gold cross-sentential links despite using a sliding window. In practice,this approach also results in a high-quality document-level parser withmanageable memory requirements. Our proposed system performs on par with thestate-of-the-art pipeline approach for document-level AMR parsing task onMulti-Sentence AMR 3.0 corpus while maintaining sentence-level parsingperformance.

Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing