← Understanding
Natural Language Processing › Understanding ›

Syntax

767 directly classified papers

Papers per year

Papers

What Does Parameter-free Probing Really Uncover? ACL 2024
A Critical Study of What Code-LLMs (Do Not) Learn ACL 2024
UD for German Poetry EMNLP 2024
RuBLiMP: Russian Benchmark of Linguistic Minimal Pairs EMNLP 2024
Toward Compositional Behavior in Neural Models: A Survey of Current Views EMNLP 2024
Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM ACL 2024
CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages EMNLP 2024
Contribution of Linguistic Typology to Universal Dependency Parsing: An Empirical Investigation EMNLP 2024
Annotating Mystery Novels: Guidelines and Adaptations EMNLP 2024
On Eliciting Syntax from Language Models via Hashing EMNLP 2024
Llamipa: An Incremental Discourse Parser EMNLP 2024
To Ask LLMs about English Grammaticality, Prompt Them in a Different Language EMNLP 2024
Multilingual and Code-Switched Sentence Ordering NAACL 2024
Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction NAACL 2024
Investigating large language models for their competence in extracting grammatically sound sentences from transcribed noisy utterances CONLL 2024
Layer-Wise Representation Fusion for Compositional Generalization AAAI 2024
What Do Transformers Know about Government? COLING 2024
Automatic sentence segmentation of clinical record narratives in real-world data EMNLP 2024
Parsing Headed Constituencies COLING 2024
Tracking linguistic information in transformer-based sentence embeddings through targeted sparsification ACL 2024
LLMSegm: Surface-level Morphological Segmentation Using Large Language Model COLING 2024
JCoLA: Japanese Corpus of Linguistic Acceptability COLING 2024
Tree Transformer’s Disambiguation Ability of Prepositional Phrase Attachment and Garden Path Effects ACL 2024
Split and Rephrase with Large Language Models ACL 2024
Distinguishing Fictional Voices: a Study of Authorship Verification Models for Quotation Attribution EACL 2024