Two Local Models for Neural Constituent Parsing
Table 2: Span representation methods.
['Model', 'English LP', 'English LR', 'English LF', 'Chinese LP', 'Chinese LR', 'Chinese LF']
We study the two span representation methods, namely the simple concatenating representation v[i,j] We investigate appropriate representations for different models on the English dev dataset. When using sr[i,j] for BinarySpan, the performance drops greatly (92.17→91.80). Similar observations can be found when replacing sr[i,j] with v[i,j] for BiaffineRule. Therefore, we use v[i,j] for the span models and sr[i,j] for the rule models in latter experiments. For English, BinarySpan acheives 92.17 LF score. The multi-class span classifier (MultiSpan) is much better than BinarySpan due to the awareness of label information. Similar phenomenon can be observed on the Chinese dataset. We also test the linear rule (LinearRule) methods. For English, LinearRule obtains 92.03 LF score, which is much worse than BiaffineRule. In general, the performances of BiaffineRule and MultiSpan are quite close both for English and Chinese.