A Common Semantic Space for Monolingual and Cross-Lingual Meta-Embeddings
Table 10: Word accuracy for Spanish and English on UD 1.2.
['Embedding', 'en', 'es']
In order to facilitate comparison with previous state-of-the-art methods, we experiment with the Universal Dependencies 1.2 data, which provides train, development and test partitions. We train the neural network off-the-shelf using the parameters used in Akbik et al. [akbik-etal-2019-pooled], tuning the system on the development data and using the test only for the final evaluation. After that we combine the Flair embeddings with other static embeddings and meta-embeddings. For this section we chose to compare our work with those embeddings that have previously been most effective in combination with Flair, namely, Glove and FastText. Furthermore, we also compare with the best previous meta-embeddings: Numberbatch and JOINTCHyb. The Baseline result reports the best published results so far for each task and dataset. Finally, we show our meta-embedding (MVM) result. Our meta-embeddings help to improve results over previous meta-embeddings. Furthermore, our best results establish new state-of-the-art performance for POS tagging using UD 1.2 for Spanish and English.