Papers with deep learning-based models
Machine Reading, Fast and Slow: When Do Models “Understand” Language? (2022.coling-1)
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| Challenge: | Existing models of reading comprehension score highly on NLU benchmarks, but they are often 'read fast', i.e. rely on shallow patterns. |
| Approach: | They propose a definition for the reasoning steps expected from a system that would be 'reading slowly' they compare that behavior with five models of the BERT family of various sizes, observed through saliency scores and counterfactual explanations. |
| Outcome: | The proposed model is compared with five models of the BERT family of various sizes, and compared using saliency scores and counterfactual explanations. |
Compositional Semantics and Inference System for Temporal Order based on Japanese CCG (2022.acl-srw)
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| Challenge: | a system for temporal order in Japanese has not been developed for linguistic inference involving temporal expressions. |
| Approach: | They propose a Japanese NLI system that considers temporal order in Japanese . they use axioms for temporal relations and automated theorem provers to perform inference involving temporal orders. |
| Outcome: | The proposed system outperforms logic-based systems and current deep learning models on Japanese datasets. |
Logical Inferences with Comparatives and Generalized Quantifiers (2020.acl-srw)
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| Challenge: | Comparative constructions pose a challenge in Natural Language Inference (NLI) Comparatives interact with quantifiers, numerals, and lexical antonyms, but a logical inference system for comparatives has not been developed for the task. |
| Approach: | They propose a compositional semantics system that maps comparative constructions to semantic representations via combinatory categorial grammar parsers and integrates it with an automated theorem proving system. |
| Outcome: | The proposed system outperforms previous logic-based systems and deep learning models on three NLI datasets. |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
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| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
| Approach: | They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task. |
| Outcome: | The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media. |
Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations (2024.eacl-long)
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| Challenge: | Recent laws like “right to explanations” have spurred research in developing interpretable models . a recent study has shown that multimodal explanations improve performance in generating textual justifications . |
| Approach: | They propose to use visual and textual modalities to explain why a given meme is cyberbullying . they use a Contrastive Language-Image Pretraining approach to generate textual justifications . |
| Outcome: | The proposed model improves performance in visual and textual explanations and identifies the visual evidence supporting a decision. |
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)
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Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding
| Challenge: | Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges. |
| Approach: | They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs. |
| Outcome: | The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges . |
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset (2020.lrec-1)
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| Challenge: | State-of-the-art deep learning-based recommender systems require large datasets to achieve their best performance. |
| Approach: | They propose to use TripAdvisor to build a large-scale hotel recommendation dataset with 50 million reviews. |
| Outcome: | The proposed dataset is the largest publicly available hotel recommendation dataset, based on TripAdvisor, with 50 million reviews. |