Papers with NP
How Much Syntactic Supervision is “Good Enough”? (2023.findings-eacl)
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| Challenge: | RNNGs with syntactic supervision underperformed RNNs with some syntaktic supervision, whereas RNNS with mild supervision achieved the best performance comparable to the state-of-the-art GPT-2-XL. |
| Approach: | They propose a method where syntactic LMs are gradually ablated from full syntatic supervision to zero syntastic supervision by preserving NP, VP, PP, SBAR nonterminal symbols. |
| Outcome: | The proposed method underperforms the RNNGs with zero syntactic supervision, and the LMs with mild syntaktic supervision perform better than the state-of-the-art GPT-2-XL. |
Implicit Discourse Relation Classification For Nigerian Pidgin (2025.coling-main)
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| Challenge: | Existing discourse parsing tools are not available for Nigerian Pidgin (NP) this task requires supervised training and requires prompting. |
| Approach: | They propose to use implicit discourse relation classification (IDRC) for Nigerian Pidgin, which requires supervised training. |
| Outcome: | The proposed framework outperforms baseline and NP IDR classifiers in f1 scores. |
How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text (2020.coling-main)
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| Challenge: | LSTMs are widely used to capture informative long-term syntactic dependencies, but how they are reflected in their internal vectors for natural text has not been adequately investigated. |
| Approach: | They analyze how syntactic dependencies are reflected in LSTM's internal gates by learning a language model where syntaktic structures are implicitly given. |
| Outcome: | The proposed model can predict whether a word is inside a phrase structure or not from a small number of components of the context-update vector. |