Papers with PP

7 papers
Enhanced Word Representations for Bridging Anaphora Resolution (N18-2)

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Challenge: Existing word representations do not capture semantic similarity for bridging anaphora resolution.
Approach: They propose to use word embeddings to capture semantic similarity by exploring syntactic structure of noun phrases.
Outcome: The proposed model achieves 30% of accuracy for bridging anaphora resolution on ISNotes corpus.
Persona Prompting as a Lens on LLM Social Reasoning (2026.eacl-long)

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Challenge: Persona prompting (PP) is increasingly used to steer large language models towards user-specific generation, but its effect on rationales remains underexplored.
Approach: They examine how LLM-generated rationales vary when conditioned on different demographic personas . they use word-level rationale annotations to measure agreement with human annotations based on PP .
Outcome: The proposed model improves classification on the most subjective task, but fails to align with real-world demographic counterparts.
Probing via Prompting (2022.naacl-main)

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Challenge: Pre-trained language models have increased the performance of data-driven natural language processing (NLP) models on a wide variety of tasks.
Approach: They propose a model-free approach to probing via prompting which formulates probing as a prompting task and combine pruning to analyze where the model stores the linguistic information in its architecture.
Outcome: The proposed approach extracts information from pre-trained models while learning much less on its own.
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.
GerEO: A Large-Scale Resource on the Syntactic Distribution of German Experiencer-Object Verbs (2022.lrec-1)

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Challenge: Psych verbs and their properties in multiple languages have ignited discussions among linguists for several decades . Psych-verbs are often considered syntactically deviant, although this has occasionally been called into question .
Approach: They propose to use a large-scale database of more than 10,000 examples for 64 verbs from a newspaper corpus annotated for several syntactic and semantic features relevant for their analysis.
Outcome: The proposed database contains 10,000 examples for 64 verbs from a newspaper corpus and includes syntactic construction, semantic stimulus type, and form of possible stimulus preposition.
LLM Performance Predictors are good initializers for Architecture Search (2024.findings-acl)

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Challenge: Large language models (LLMs) have diverse applications, encompassing both open-ended tasks (e.g., brainstorming and chat) and closed-ended ones (eg. question answering).
Approach: They construct PP prompts for Large Language Models (LLMs) that estimate the performance of specific deep neural network architectures on downstream tasks.
Outcome: The proposed model achieves a SoTA mean absolute error and a slight degradation in rank correlation coefficient compared to baseline predictors in machine translation tasks.
Speech Corpus for Korean Children with Autism Spectrum Disorder: Towards Automatic Assessment Systems (2024.lrec-main)

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Challenge: Despite the growing demand for digital therapeutics for children with autism spectrum disorder, there is currently no speech corpus for Korean children with ASD.
Approach: They propose to use Korean children with ASD to improve pronunciation and severity evaluation by transcribed speech and language evaluation sessions to assess their articulatory and linguistic characteristics.
Outcome: The proposed corpus will be 300 children with ASD and 50 typically developing (TD) children.

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