Papers by Gerald Penn
Rationally Reappraising ATIS-based Dialogue Systems (P19-1)
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| Challenge: | Recent state-of-the-art neural models have obtained F1-scores near 98% on the task of slot filling. |
| Approach: | They propose to fix annotation errors in ATIS and propose a rule-based grammar for slot filling that achieves a 95.82% F1 score. |
| Outcome: | The proposed grammar achieves a 95.82% F1-score on the ATIS domain. |
Does BERT Rediscover a Classical NLP Pipeline? (2022.coling-1)
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| Challenge: | Existing theories of BERT's structure lack conclusive empirical support . however, there is scepticism about the premises of probing itself . |
| Approach: | They propose a new probe called GridLoc that can take into account token positions, training rounds, and random seeds. |
| Outcome: | The proposed probe detects other, stronger regularities suggesting appeals to layer depth may not be the preferable mode of explanation for BERT’s inner workings. |
Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing methods such as LoRA and VeRA use memory-efficient methods to fine-tune large language models. |
| Approach: | They propose a method that uses only 1–5% of the standard LoRA parameters and achieves state-of-the-art performance across a wide range of tasks. |
| Outcome: | The proposed method achieves state-of-the-art performance across a wide range of tasks using only 1–5% of the standard LoRA parameters. |
Inside-Outside Algorithm for Probabilistic Product-Free Lambek Categorial Grammar (2025.coling-main)
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| Challenge: | Many studies have discovered hidden syntactic structures within language models without the guidance of explicit rules. |
| Approach: | They propose an inside-outside algorithm for Probabilistic Lambek Categorical Grammar. |
| Outcome: | The proposed algorithm is used in the estimation of probabilistic context-free grammars. |
LCGbank: A Corpus of Syntactic Analyses Based on Proof Nets (2024.lrec-main)
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| Challenge: | Recent studies have focused on statistical syntactic parsing with proof nets . however, there has been a paucity of corpora in formalisms for which proof net is applicable . |
| Approach: | They propose a corpus of syntactic analyses based on Lambek categorial grammar . they leverage the relationship between LCG and CCG to address this problem . |
| Outcome: | The proposed method exploits the relationship between LCG and CCG to build an English-language corpus of syntactic analyses based on proof nets . the results suggest that the proposed method is weakly context-free equivalent and NP-complete . |
Temporal Histories of Epidemic Events (THEE): A Case Study in Temporal Annotation for Public Health (2020.lrec-1)
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| Challenge: | Current EBS estimates the occurrence time of events based on coarse metadata such as document publication time. |
| Approach: | They propose a temporal annotation standard THEE-TimeML and a corpus TheeBank . they document the corpus annotation process and demonstrate the immediate benefit . |
| Outcome: | The proposed standards are based on the existing timeML and the corpus TheeBank . the proposed standards demonstrate the immediate benefit to public health applications . |
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings (2021.eacl-main)
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| Challenge: | a new method for compressing word vector embeddings into integers is being developed . a high precision approach to compressing words into integer results in negligible performance gains . |
| Approach: | They propose a method for compressing word vector embeddings into integers using the Chinese Reminder Theorem. |
| Outcome: | The proposed method speeds up addition by 48.27% and compresses GloVe word embedding libraries by 25.86%. |
Reanalyzing the Most Probable Sentence Problem: A Case Study in Explicating the Role of Entropy in Algorithmic Complexity (2021.eacl-main)
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| Challenge: | Existing descriptive complexity measures are ineffective at describing algorithms' behaviour, and can make an apparently tractable problem seem NP-complete. |
| Approach: | They propose to use statistical measures to give an updated analysis of the complexity of the NP-complete most probable sentence problem for pCFGs. |
| Outcome: | The proposed method can be applied to word sense disambiguation and inference tasks. |
LLM-supertagger: Categorial Grammar Supertagging via Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent studies have shown that LLMs are underperforming in classification tasks due to their decoder-based nature. |
| Approach: | They propose a method that significantly boosts LLMs' performance in supertagging for both Combinatory Categorial Grammar (CCG) and Lambek Categorian Grammar (LCG). |
| Outcome: | The proposed method outperforms LSTM and encoder-based models and achieves state-of-the-art performance. |
A Generative Model for Lambek Categorial Sequents (2024.lrec-main)
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| Challenge: | generative models such as PLC+ generate grammatical sentences with a high probability of being grammatized. |
| Approach: | They propose a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. |
| Outcome: | The proposed model generates Lambek Categorial Grammar(LCG) sequents and is more robust to probabilistic context-free grammars. |
ConTempo: A Unified Temporally Contrastive Framework for Temporal Relation Extraction (2024.findings-acl)
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| Challenge: | Temporal relation extraction (TRE) is a task of classifying temporal relations between events conveyed in narratives. |
| Approach: | They propose a Temporally Contrastive learning model that increases the model’s awareness of the meaning of temporal relations by leveraging their symmetric or antisymmetric properties. |
| Outcome: | The proposed model improves the model's representation of meaning of temporal relations and its ability to integrate with the underlying temporal calculus. |
Sheaf Discovery with Joint Computation Graph Pruning and Flexible Granularity (2025.emnlp-main)
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| Challenge: | Experimental results show that DiscoGP extracts sheaves that preserve 93-100% of a model’s performance while comprising only 1-7% of the original weights and connections. |
| Approach: | They propose a framework for extracting self-contained modular units within neural language models (LMs) they use a gradient-based pruning algorithm to prune the original LM to a sparse skeleton . |
| Outcome: | The proposed framework preserves 93-100% of the original model's performance while preserving only 1-7% of the model''s original weights and connections. |
Decomposed scoring of CCG dependencies (2023.acl-short)
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| Challenge: | a standard evaluation of supertagging errors can result in disproportionate penalization of supertaggers . comparative categorial grammar (ccg) supertaggers can adjust for their own errors to keep sentences parsable . |
| Approach: | They propose a decomposed scoring method based on subcategorial labels to address this problem. |
| Outcome: | The proposed method penalizes supertagging errors and obfuscates erroneous dependencies . the proposed method is based on subcategorial labels . |
FAB: The French Absolute Beginner Corpus for Pronunciation Training (2020.lrec-1)
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| Challenge: | French Absolute Beginner corpus is intended for the development and study of Computer-Assisted Pronunciation Training (CAPT) tools for absolute beginner learners. |
| Approach: | They introduce the French Absolute Beginner (FAB) speech corpus which is intended for the development and study of Computer-Assisted Pronunciation Training tools for absolute beginner learners. |
| Outcome: | The proposed corpus is intended for the development and study of Computer-Assisted Pronunciation Training tools for absolute beginner learners. |