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6 Parsing with EDUs
Figure 7 shows first few decoding steps with final parsers with EDUs.
Figure 7: Our discourse parser along with the first few decoding steps. When EDUs are given, we use a hierarchical EDU encoding (Boundary LSTM) to model EDU boundary representations.
We show our best parser's (with gold EDUs) confusion matrix for all relation labels in Fig. 5. Fig. 9 - 11 present examples where our parser falsely labels a Condition as Background, Temporal as Joint and Explanation as Elaboration.
Figure 8: Confusion matrix for relation labels on the RST-DT test set. The vertical axis represents true and horizontal axis represents predicted relations. The relations are: Joint (JO), Elaboration (EL), Temporal (TEM), Contrast (CONT), Cause (CA), Same-Unit (SA), Manner-Means (MA), Attribution (AT), Condition (COND), Comparison (COMP), Background (BA), Enablement (EN), Explanation (EX), Evaluation (EV), Summary (SU), Topic-Comment (T-CM), Topic-Change (T-C) and TextualOrganization (T-O).
Condition [Background]
The next time you hear a Member of Congress moan about the deficit , consider what Congress did Friday .
Figure 9: Our system incorrectly labels a Condition as Background.
Temporal [Joint]
Three new issues begin trading on the New York Stock Exchange today , and one began trading on the Nasdaq/National Market System last week .
Figure 10: Our system incorrectly labels a Temporal as Joint.
Explanation [Elaboration]
The top stock-index arbitrage firm last month was Morgan Stanley & Co . Of Morgan Stanley ' s 66.8 million shares in program trades for the month , 53.1 million were in stock-index arbitrage trades .
Figure 11: Our system incorrectly labels a Explanation as Elaboration.