Papers with MG
Constraining MGbank: Agreement, L-Selection and Supertagging in Minimalist Grammars (P18-1)
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| Challenge: | a deep grammatical formalism that has not been applied to NLP tasks is the Minimalist Grammar (MG) formalism. |
| Approach: | They propose to extend the Minimalist Grammar (MG) formalism with a mechanism for enforcing fine-grained selectional restrictions and agreements. |
| Outcome: | The proposed system is compatible with Markovian supertaggers and enables efficient parsing on key dependency types. |
Double Graph Based Reasoning for Document-level Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction fail to recognize relations between entities across sentences. |
| Approach: | They propose a method to recognize relations for long paragraphs by a Graph Aggregation-and-Inference Network (GAIN) they propose to use a heterogeneous mention-level graph and an entity-level EG graph to analyze the relationships. |
| Outcome: | The proposed method achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. |
Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge (P19-1)
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| Challenge: | Existing methods for Chinese relation extraction suffer from segmentation errors and ambiguity of polysemy. |
| Approach: | They propose a multi-grained lattice framework for Chinese relation extraction . they incorporate word-level information into character sequence inputs to avoid segmentation errors . |
| Outcome: | The proposed model outperforms existing models on three real-world datasets in distinct domains. |
User Memory Reasoning for Conversational Recommendation (2020.coling-main)
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| Challenge: | Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations. |
| Approach: | They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG. |
| Outcome: | The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances. |