Papers by John Torr
Wide-Coverage Neural A* Parsing for Minimalist Grammars (P19-1)
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| Challenge: | a new parser for wide-coverage parsing uses a linguistically expressive yet highly constrained grammar . the expected time complexity of the parsers is cubic in the length of the sentence . |
| Approach: | They propose to use a linguistically expressive yet highly constrained grammar to parse a wide-coverage sentence using a bi-LSTM neural network supertagger. |
| Outcome: | The proposed algorithm recovers unbounded long distance dependencies and can recover unbundled long distance dependents. |
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. |
LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues (2024.naacl-srw)
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Joe Stacey, Jianpeng Cheng, John Torr, Tristan Guigue, Joris Driesen, Alexandru Coca, Mark Gaynor, Anders Johannsen
| Challenge: | Existing datasets with limited domain coverage and few challenging conversational phenomena are often unlabelled . Existing data is limited in quality and lacks a robust evaluation process . |
| Approach: | They propose a high quality data generation system that generates high quality dialogues using 4,277 conversations across 100 intents. |
| Outcome: | The proposed system produces high quality dialogue data with high quality labels. |