We lifted 4 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| bplank/bilstm-aux | canonical | 1 of 1 |
| ilyagusev/rnnmorph | pwc_unofficial | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| drop | Ran | bplank/bilstm-aux/src/structbilty.py pointer only (licence: NOASSERTION) · get_code("7cd9c10b78a3b0e1") |
| build_dense_chars_layer | Not yet run | ilyagusev/rnnmorph/rnnmorph/char_embeddings_model.py code served (permissive licence) · get_code("38d94fe63b23b68c") |
| convert_from_opencorpora_tag | Not yet run | ilyagusev/rnnmorph/rnnmorph/data_preparation/process_tag.py code served (permissive licence) · get_code("6e1bbd301beb5989") |
| process_gram_tag | Not yet run | ilyagusev/rnnmorph/rnnmorph/data_preparation/process_tag.py code served (permissive licence) · get_code("90ae33fed83d4f02") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful for various NLP sequence modeling tasks, but little is known about their reliance to input representations, target languages, data set size, and label noise. We address these issues and evaluate bi-LSTMs with word, character, and unicode byte embeddings for POS tagging. We compare bi-LSTMs to traditional POS taggers across languages and data sizes. We also present a novel bi-LSTM model, which combines the POS tagging loss function with an auxiliary loss function that accounts for rare words. The model obtains state-of-the-art performance across 22 languages, and works especially well for morphologically complex languages. Our analysis suggests that bi-LSTMs are less sensitive to training data size and label corruptions (at small noise levels) than previously assumed.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1604.05529")
get_code_for_paper("1604.05529")
have("1604.05529")
Connect an agent — have() is free.