Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document
  AMR Parsing
Sadhana Kumaravel Tahira Naseem Ramon Fernandez Astudillo Radu Florian Salim Roukos
The sliding window approach provides an elegant way to handle contexts of
sizes larger than the Transformer's input window, for tasks like language
modeling. Here we extend this approach to the sequence-to-sequence task of
document parsing. For this, we exploit recent progress in transition-based
parsing to implement a parser with synchronous sliding windows over source and
target. We develop an oracle and a parser for document-level AMR by expanding
on Structured-BART such that it leverages source-target alignments and
constrains decoding to guarantee synchronicity and consistency across
overlapping windows. We evaluate our oracle and parser using the Abstract
Meaning Representation (AMR) parsing 3.0 corpus. On the Multi-Sentence
development 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 with
manageable memory requirements. Our proposed system performs on par with the
state-of-the-art pipeline approach for document-level AMR parsing task on
Multi-Sentence AMR 3.0 corpus while maintaining sentence-level parsing
performance.