Papers with synchronized training
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings (P18-1)
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| Challenge: | recurrent neural networks have produced significant advances in part-of-speech tagging accuracy . a common feature of these models is the presence of rich initial word encodings . however, word or sub-word information interacts only through subsequent recursive layers . |
| Approach: | They propose to use recurrent neural networks with sentence-level context for initial character and word-based representations. |
| Outcome: | The proposed model has the highest accuracy of all participating systems in the CoNLL 2017 task. |
Reproducing a Morphosyntactic Tagger with a Meta-BiLSTM Model over Context Sensitive Token Encodings (2020.lrec-1)
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| Challenge: | Reproducibility of research results is only recently beginning to be practiced and acknowledged . a research culture that focuses on beating previous benchmarks while disregarding the need to contribute to scientific knowledge and understanding is a problem, says a researcher. |
| Approach: | They reproduced work on morphosyntactic tagging using a meta-model . they did not contact the original authors for reproduction . |
| Outcome: | The proposed model outperforms previous models on morphological tagging tasks but fails to match the F1-scores reported for the meta-BiLSTM model. |