Papers with syntactic and semantic tasks
Phylogeny-Inspired Adaptation of Multilingual Models to New Languages (2022.aacl-main)
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| Challenge: | Large pretrained multilingual models have delivered promising results due to cross-lingual learning capabilities on a variety of language tasks. |
| Approach: | They propose to use language phylogenetic information to improve cross-lingual transfer by leveraging closely related languages in a structured, linguistically-informed manner. |
| Outcome: | The proposed model significantly improves on the baseline model on languages unseen during training. |
When Beards Start Shaving Men: A Subject-object Resolution Test Suite for Morpho-syntactic and Semantic Model Introspection (2020.coling-main)
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| Challenge: | In German, subject-object resolution is the second most error-prone task in syntactic parsing, after prepositional phrase attachment. |
| Approach: | They introduce the SORTS Subject-Object Resolution Test Suite of German minimal sentence pairs for model introspection. |
| Outcome: | The proposed test suite is based on the annotations of 8,502 transitive clauses with 8 word order patterns, 5 morphological and syntactic and 11 semantic property classes. |
Learning from Child-directed Speech in Two-language Scenarios: A French-English Case-Study (2026.findings-eacl)
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| Challenge: | a systematic study of compact language models with limited computational resources is challenging for many research contexts and real-world applications. |
| Approach: | They extend BabyBERTa to English-French scenarios under strictly sizematched data conditions. |
| Outcome: | The proposed model extends to English-French scenarios under sizematched data conditions . the results show context-dependent effects of multilingual training . |
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis (D19-1)
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| Challenge: | despite its practical advantages, transductive learning is underexplored in natural language processing . despite the simplicity of the technique, it is understudied in natural languages . |
| Approach: | They conduct an empirical study of transductive learning for neural models . they fine-tune language models on an unlabeled test set to obtain test-set-specific word representations. |
| Outcome: | The proposed method improves state-of-the-art neural models in syntactic and semantic tasks. |