Papers with linguistic representations
Deep Learning Approaches to Text Production (N18-6)
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| Challenge: | Text production is a key component of many NLP applications . Claire Gardent is based in France and is pursuing research in text production . |
| Approach: | This tutorial will cover the fundamentals and state-of-the-art research on neural models for text production. |
| Outcome: | This tutorial will cover the fundamentals and the state-of-the-art research on neural models for text production. |
Contrasting distinct structured views to learn sentence embeddings (2021.eacl-srw)
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| Challenge: | Existing methods to build sentence embeddings rely on a similar Recurrent Neural Network (RNN) heterogeneity of performances across models and tasks makes us assume some structures might be better adapted given the considered task or sentence. |
| Approach: | They propose a self-supervised method that builds sentence embeddings from syntactic structures . they hypothesize that some linguistic representations might be better adapted given the task . |
| Outcome: | The proposed method outperforms comparable methods on several tasks from standard sentence embedding benchmarks. |
LMSOC: An Approach for Socially Sensitive Pretraining (2021.findings-emnlp)
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| Challenge: | Large-scale pretraining models have been shown to learn effective linguistic representations for many NLP tasks, but there are many real-world contextual aspects of language that current approaches do not capture. |
| Approach: | They propose to integrate speaker social context into the learned representations of large-scale language models by using graph representation learning algorithms and primed language model pretraining with these social context representations. |
| Outcome: | The proposed approach improves on geographically sensitive language modeling tasks by more than 100% relative lift on MRR compared to baselines. |
Towards Explainable Evaluation of Language Models on the Semantic Similarity of Visual Concepts (2022.coling-1)
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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. |
Automatic Readability Assessment for Closely Related Languages (2023.findings-acl)
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| Challenge: | In recent years, the main focus of research on automatic readability assessment (ARA) has shifted towards using expensive deep learning-based methods with the primary goal of increasing models’ accuracy. |
| Approach: | They focus on how linguistic aspects such as mutual intelligibility or degree of language relatedness can improve ARA in a low-resource setting. |
| Outcome: | The inclusion of CrossNGO, a novel feature exploiting n-gram overlap, significantly improves the performance of ARA models compared to the use of off-the-shelf large multilingual language models alone. |
NICE: Neural Image Commenting with Empathy (2021.findings-emnlp)
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Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, Jianfeng Wang, Kenneth Forbus, Jianfeng Gao
| Challenge: | Emotion and empathy are examples of human qualities lacking in many human-machine interactions. |
| Approach: | They propose to generate images with human-generated comments with enhanced emotion and empathy while minimizing inappropriate or offensive outputs. |
| Outcome: | The proposed model generates more human-like and engaging image comments on two images with human-generated comments and human annotations while minimizing inappropriate or offensive outputs. |
Linguistic representations for fewer-shot relation extraction across domains (2023.acl-long)
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| Challenge: | Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolds on performance in several NLP tasks. |
| Approach: | They extend previous work to examine whether linguistic representations enhance generalizability . they incorporate syntactic and semantic graphs from off-the-shelf tools into a transformer-based architecture . |
| Outcome: | The proposed approach enhances generalization by providing cross-domain pivots . it also shows that syntactic and semantic graphs exhibit roughly equivalent utility . |
Layered Insights: Generalizable Analysis of Human Authorial Style by Leveraging All Transformer Layers (2025.emnlp-main)
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| Challenge: | Existing approaches to authorship attribution model only learn from the output layer of pre-trained transformers, ignoring representations learned at other layers. |
| Approach: | They propose a model that leverages the various linguistic representations learned at different layers of pre-trained transformer-based models to model the authorship attribution task more effectively. |
| Outcome: | The proposed model performs better on out-of-domain and in-domain scenarios, while ignoring representations learned at other layers. |
Large Language Models Are Partially Primed in Pronoun Interpretation (2023.findings-acl)
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| Challenge: | Existing studies suggest large language models acquire rich linguistic representations, but little is known about whether they adapt to linguistic biases in a human-like way. |
| Approach: | They examine whether large language models display human-like referential biases using stimuli and procedures from real psycholinguistic experiments. |
| Outcome: | The proposed models display human-like referential biases when exposed to referential patterns in the local context. |
Emergent morpho-phonological representations in self-supervised speech models (2025.emnlp-main)
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| Challenge: | a recent study shows that self-supervised speech models do not represent phonological and morphological phenomena in frequent English noun and verb inflections. |
| Approach: | They study how S3Ms represent phonological and morphological phenomena in English . they propose alternative representational strategies that may support human spoken word recognition . |
| Outcome: | a new study shows that S3M models can represent phonological and morphological phenomena in English . the models can be trained to recognize spoken words in naturalistic, noisy environments . |
Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity (2026.findings-acl)
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| Challenge: | a recent study has shown that human-like working memory constraints can be integrated into the Transformer architecture . our model incorporates fixed-width windows and temporal decay based attention mechanisms . |
| Approach: | They propose to integrate working memory constraints into the Transformer architecture . they use fixed-width windows and temporal decay-based attention mechanisms . |
| Outcome: | The proposed models show that they can learn better when training data is scarce . the findings suggest that such constraints may serve as a beneficial bias guiding models towards more robust representations . |