Papers by Jakob Prange
Comprehensive Supersense Disambiguation of English Prepositions and Possessives (P18-1)
Copied to clipboard
Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend
| Challenge: | Frequent prepositions like for are maddeningly polysemous, their interpretation depends especially on the object of the preposition. |
| Approach: | They propose a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. |
| Outcome: | The proposed annotations are comprehensive with respect to types and tokens of these markers and use broadly applicable supersense classes rather than fine-grained dictionary definitions. |
Cross-lingual Semantic Representation for NLP with UCCA (2020.coling-tutorials)
Copied to clipboard
| Challenge: | introductory tutorial to UCCA, a symbolic meaning representation for semantic representations. |
| Approach: | This tutorial introduces UCCA, a cross-linguistically applicable framework for semantic representation . it will provide a detailed introduction to the UCca annotation guidelines, design philosophy and available resources . |
| Outcome: | The tutorial will provide a detailed introduction to the UCCA framework and compare it to other meaning representations. |
Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures (2023.starsem-1)
Copied to clipboard
| Challenge: | a recent attempt at language modeling with predicted semantic structure failed to establish empirical lower bounds on what could have made the attempt successful. |
| Approach: | They propose a concise binary vector representation of semantic structure at the lexical level and evaluate how good an incremental tagger needs to be to achieve better-than-baseline performance. |
| Outcome: | The proposed model can achieve better-than-baseline performance without losing its main advantages and lower bounds on prediction quality can't be established via a single score alone. |
Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics (2020.coling-main)
Copied to clipboard
| Challenge: | a systematic comparative analysis of linguistic meaning representations from different frameworks is needed. |
| Approach: | They compare a rule-based converter and a supervised delexicalized parser to map meaning representations from different frameworks. |
| Outcome: | The proposed method yields surprisingly accurate representations close to fully supervised UCCA parser quality. |
Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model (2023.acl-long)
Copied to clipboard
| Challenge: | We use both Bayesian and neural models to dissect a data set of Chinese learners’ pre- and post-interventional responses to two tests measuring their understanding of English prepositions. |
| Approach: | They use Bayesian and neural models to dissect Chinese learners' responses to two tests measuring their understanding of English prepositions. |
| Outcome: | The proposed model can predict grammaticality and learnability based on language model probabilities. |
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way. |
| Approach: | They examine whether syntactic and semantic graph representations can complement and improve neural language modeling. |
| Outcome: | The proposed model outperforms pretrained models on English WSJ in perplexity and other metrics. |
Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories (2021.tacl-1)
Copied to clipboard
| Challenge: | Combinatory Categorial Grammar (CCG) parsers operate as a pipeline with a large search space of complex 'supertags' . |
| Approach: | They propose to use CCG supertags to generate CCG categories that have never been seen in training and to use tree-structured prediction to account for their internal structure. |
| Outcome: | The proposed model recovers a fraction of the long-tail supertags while approximating the state of the art in overall tag accuracy with fewer parameters. |