Evaluation Measures Following standard practice, we use Unlabeled Attachment Score ( UAS ) as the evaluation metric in all our experiments.

We report UAS excluding punctuation on CoNLL datasets, following Martins et al.

For the CATiB dataset, we report UAS including punctuation in order to be consistent with the published results in the 2013 SPMRL shared task (Seddah et al., 2013).

Moreover, our model also outperforms the 88.80% average UAS reported in Martins et al.

With these features our model achieves an average UAS 89.28%.

UAS POS Acc.

Evaluation metrics: we evaluate our parsing systems by using the standard metrics for dependency parsing: Labeled Attachment Score (LAS) and Unlabeled Attachment Score ( UAS ), computed using all tokens including punctuation.

In the “labeled representation” evaluation, the UAS provides a measure of syntactic attachments for sequences of words, independently of the (regular) MWE status of subse-quences.

The UAS for labeled representation will be maximal, whereas for the flat representation, the last two tokens will count as incorrect for UAS .

We also obtain the best published UAS results on 5 languages.1

As the evaluation measure, we use unlabeled attachment scores ( UAS ) excluding punctuation.

Our model also achieves the best UAS on 5 languages.

Figure 1 shows the average UAS on CoNLL test datasets after each training epoch.

Figure 1: Average UAS on CoNLL testsets after different epochs.

We measure parsing performance using the standard unlabeled attachment score ( UAS ), excluding punctuation marks.

Table 4: UAS comparison on English test data.

UAS Li et al.

Specifically, we measure the similarity, sim(ug), 103), between the source domain distributions of ua) and w, and select the top 7“ similar neighbours ua ) for each word 21) as additional features for 212.

The value of a neighbour ua ) selected as a distributional feature is set to its similarity score sim(ug), 103).

At test time, for each word 21) that appears in a target domain test sentence, we measure the similarity, sim(Mug), 107), and select the most similar 7“ words ua ) in the source domain labeled sentences as the distributional features for 212, with their values set to sim(Mug), wT).

Table 3: UAS for two versions of our approach, together with baseline and oracle systems on Google Universal Treebanks version 1.0.

Table 4: UAS for two versions of our approach, together with baseline and oracle systems on Google Universal Treebanks version 2.0.

Parsing accuracy is measured with unlabeled attachment score ( UAS ): the percentage of words with the correct head.

Moreover, our approach considerably bridges the gap to fully supervised dependency parsers, whose average UAS is 84.67%.

Table 5 illustrates the UAS of our approach trained on different amounts of parallel data, together with the results of the projected transfer parser re-implemented by us (PTPT).