Works of Conference on Computational Natural Language Learning (CoNLL)
- Sense and Sensitivity: “Reasoning” Models are More Robust, but can Diverge from Human Consensus in a Legal Interpretation TaskDawson Petersen, Abhishek Purushothama, Nathan Schneider2026 · 1 citation
- Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus ConstructionsWesley Scivetti, Ethan Gotlieb Wilcox, Nathan Schneider, et al.2026 · 1 citation
- Addressing the Ecological Fallacy in Larger LMs with Human ContextNikita Soni, Dhruv Vijay Kunjadiya, Pratham Piyush Shah, et al.2026 · 1 citation
- Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual ReasoningMohit Vaishnav, Tanel Tammet2026 · 1 citation
- What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap DependenciesZhenghao Zhou, William Dai, Maya Viswanathan, et al.2026 · 1 citation
- Bridging the Socioeconomic Gap in Education: A Hybrid AI and Human Annotation ApproachNahed Abdelgaber, Labiba Jahan, Arham Vinit Doshi, et al.2025 · 1 citation
- Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible DataBadr AlKhamissi, Yingtian Tang, Abdülkadir Gökce, et al.2024 · 1 citation
- Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared TaskAmir More, Reut Tsarfaty2017 · 1 citation
- Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language ModelsNura Aljaafari, Danilo Carvalho, Andre Freitas2026
- Child-directed speech facilitates production, not comprehension, in BabyLMsBastian Bunzeck, Sina Zarrieß2026
- A Method for Learning Large-Scale Computational Construction Grammars from Semantically Annotated CorporaPaul Van Eecke, Katrien Beuls2026
- Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networksClaire Hobbs, R. Thomas McCoy2026
- What Brain Data Adds to Language Model TrainingGabriele Merlin, Omer Moussa, Mariya Toneva2026
- A framework for analyzing concept representations in neural modelsBurin Naowarat, Hao Tang, Sharon Goldwater2026
- Word predictability estimates from language models are not robust to tokenizer vocabularyKien Nguyen, Suhas Arehalli2026
- A Dataset for Oral Reading in Young English ReadersMadison Rose, Michael Bennie, Valeria Pagliai, et al.2026
- Metric Grammars: A usage-based grammatical formalism that supports generation, parsing and morphological innovationWhitney Tabor, Hyosun Lee2026
- ThinkStruct: RST-Aware Attention for Logical Reasoning in Machine Reading ComprehensionNhi Thao Tran, Tien Le, Toan Pham, et al.2026
- Logic-Level Evaluation of Logical Table-to-Text GenerationLena Trigg, Dean F. Hougen, Ahsan Bilal2026
- CAIT: A Syntactic Parsing Toolkit for Child–Adult InTeractionsFrancesca Padovani, Xiulin Yang, Bastian Bunzeck, et al.2026
- Similar Predictions, Different Processes: A Multi-Level Comparison of Human and Multimodal LLM Language PredictionShuqi Wang, Zhenguang Cai2026
- Who Generates More Empathetic Responses–-Humans or LLMs? A Comparative Evaluation with Human and LLM JudgesAnuradha Welivita, Fawzia Zeitoun, Pearl Pu2026
- Event-Guided Slot Interaction for Multi-Domain Dialogue State TrackingYing Xia, Wei Liu2026
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