| Challenge: | Using machine learning models, we compared the semantic space of university-level students learning French with native speakers' (L1) . |
| Approach: | They extracted semantic features from narrative text and used interpretability techniques to identify the most informative features per model. |
| Outcome: | The results show that the second language learners had higher semantic similarity scores than the native speakers at the token level, whereas the similarity decreased over time but did not reach native-level values. |
Similar Papers
Tomato, Tomahto, Tomate: Do Multilingual Language Models Understand Based on Subword-Level Semantic Concepts? (2025.findings-naacl)
Copied to clipboard
| Challenge: | a recent study shows that human understanding of text depends on general semantic concepts of words that are robust to their superficial forms. |
| Approach: | They evaluate the accuracy of multilingual multilingual language models based on subword-level semantics . they form "semantic tokens" by merging semantically similar subwords and embeddings based upon the results . |
| Outcome: | The proposed models are able to make predictions on multilingual tasks with different tokenizers and model sizes. |
Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces (2023.emnlp-main)
Copied to clipboard
| Challenge: | Conceptual spaces are constructed from a set of quality dimensions, which are usually learned from human judgements, which means that applications of conceptual spaces are limited to narrow domains. |
| Approach: | They propose to use Large Language Models to learn perceptually grounded representations by comparing them to larger models of the BERT family. |
| Outcome: | The proposed models outperform the largest model, despite being 2 to 3 orders of magnitude smaller. |
A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces (2023.acl-long)
Copied to clipboard
| Challenge: | Existing paradigms for the linguistically oriented exploration of large neural language models include treating the model as a linguistic test subject by measuring output on test sentences and building probing classifiers on top of embeddings to test whether the embeddables are sensitive to certain properties like dependency structure. |
| Approach: | They project contextual embeddings into interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. |
| Outcome: | The proposed method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes. |
Semantic Parsing for English as a Second Language (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies on domain adaptation in NLP focus on learning challenges at the syntax-semantics interface during second language acquisition. |
| Approach: | They propose to use English Resource Grammar and TLE to parse ESL data using a reranking model to evaluate the quality of the annotations. |
| Outcome: | The proposed model can obtain a very promising quality in comparison to human annotations. |
Towards Explainable Evaluation of Language Models on the Semantic Similarity of Visual Concepts (2022.coling-1)
Copied to clipboard
Maria Lymperaiou, George Manoliadis, Orfeas Menis Mastromichalakis, Edmund G. Dervakos, Giorgos Stamou
| Challenge: | Recent advances in NLP research have focused on robustness and explainability issues of their evaluation strategies. |
| Approach: | They propose to use pre-trained transformers to evaluate semantic similarity for visual vocabularies . they propose to provide explainable metrics for understanding the quality of retrieved instances . |
| Outcome: | The proposed metrics highlight inabilities of widely used evaluation methods and highlight weaknesses in learned linguistic representations. |
When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training (2026.eacl-long)
Copied to clipboard
| Challenge: | Recent studies have found that Large Language Models process multilingual inputs in shared concept spaces, thought to support generalization and cross-lingual transfer. |
| Approach: | They investigate the development of language-agnostic concept spaces during pretraining of EuroLLM using the causal interpretability method of activation patching. |
| Outcome: | The proposed model is language-agnostic and enables cross-lingual transfer . the model is able to process multilingual inputs, but lacks cross-linguistic alignment . |
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies (2024.findings-acl)
Copied to clipboard
| Challenge: | Conceptual spaces represent entities in terms of their primitive semantic features. |
| Approach: | They argue that conceptual spaces should be used alongside knowledge graphs in many settings to model entities in terms of their primitive semantic features. |
| Outcome: | The proposed model can rank entities according to a given conceptual space dimension but ground truth rankings for conceptual space dimensions are rare. |
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Contemporary advances in NLP are built on the representational power of latent embedding spaces learned by self-supervised language models (LMs). |
| Approach: | They use a new information theoretic probing suite to analyze representational subspaces in language models. |
| Outcome: | The proposed approach compared performance of nine tasks across 2M pre-training steps and five seeds. |
Using Visual Feature Space as a Pivot Across Languages (2020.findings-emnlp)
Copied to clipboard
| Challenge: | We show that models trained to generate textual captions in more than one language can leverage their jointly trained feature space during inference to pivot across languages. |
| Approach: | They show that models trained to generate captions in more than one language can leverage their jointly trained feature space during inference to pivot across languages. |
| Outcome: | The proposed approach improves quality of captions in German and English by leveraging captions from a second language. |
Investigating Productive and Receptive Knowledge: A Profile for Second Language Learning (C18-1)
Copied to clipboard
| Challenge: | Literature on receptive and productive vocabulary often ignores grammar in second language acquisition studies. |
| Approach: | They use two corpora to investigate divergences in grammatical structures in texts . they set a polarity to the divergence scores to indicate whether there is overuse or underuse . |
| Outcome: | The proposed system will help language learners to activate more of their passive knowledge in writing texts. |