AMR Parsing as Graph Prediction with Latent Alignment
Table 3: F1 scores of on subtasks. Scores on ablations are averaged over 2 runs. The left side results are from LDC2015E86 and right results are from LDC2016E25.
['Metric', 'Pre- Align', 'R1', 'Pre- Align', 'R2 mean']
First, we would like to see if our latent alignment framework is beneficial. In order to test this, we create a baseline version of our system (‘pre-align’) which relies on the JAMR aligner Flanigan et al. Recall that in our model we used training data and a lemmatizer to produce candidates for the concept prediction model (see In order to have a fair comparison, if a concept is not aligned after JAMR, we try to use our copy function to align it. If an alignment is not found, we make the alignment uniform across the unaligned words. In preliminary experiments, we considered alternatives versions (e.g., dropping concepts unaligned by JAMR or dropping concepts unaligned after both JAMR and the matching heuristic), but the chosen strategy was the most effective. These scores of pre-align are superior to the results from \newcitefoland-martin:2017: Long which also relies on JAMR alignments and uses BiLSTM encoders. There are many potential reasons for this difference in performance. For example, their relation identification model is different (e.g., single pass, no bi-affine modeling), they used much smaller networks than us, they use plain JAMR rather than a combination of JAMR and our copy function, they use a different recategorization system. These results confirm that we started with a strong basic model, and that our variational alignment framework provided further gains in performance.