| Inference | For each pair of predicates, we search for clusterings to maximize the sum of the log-probability and the negated penalty term. |
| Introduction | For predicates present in both sides of a bitext, we guide models in both languages to prefer clusterings which maximize agreement between predicate argument structures predicted for each aligned predicate pair. |
| Monolingual Model | Now, when parameters and argument key clusterings are chosen, we can summarize the remainder of the generative story as follows. |
| Problem Definition | The objective of this work is to improve argument key clusterings by inducing them simultaneously in two languages. |
| Clustering Methods, Evaluation Metrics and Experimental Setup | We make use of these processes in all our experiments and systematically compute cluster labelling and feature maximisation on the output clusterings . |
| Clustering Methods, Evaluation Metrics and Experimental Setup | As we shall see, this permits distinguishing between clusterings with similar F-measure but lower “linguistic plausibility” (cf. |
| Clustering Methods, Evaluation Metrics and Experimental Setup | Following (Sun et al., 2010), we use modified purity (mPUR); weighted class accuracy (ACC) and F-measure to evaluate the clusterings produced. |