Working Hard or Hardly Working:Challenges of Integrating Typology into Neural Dependency Parsers
Table 2: Performance of typology prediction using hidden states of the parser’s encoder, compared to a majority baseline which predicts the most frequent category.
['WALS ID', '82A', '83A', '85A', '86A', '87A', '88A']
This can likely be attributed to the training routine, which poses two inherent difficulties: 1) the parser has few examples (entire languages) to generalize from, making it hard from a learning perspective and 2) a naïve encoder can already implicitly capture important typological features within its hidden state, using only the surface forms of the input. This renders the additional typology features redundant. We find they are nearly linearly separable — logistic regression achieves greater than 90% accuracy on average.