Papers with CNs
Do Vision-Language Models Understand Compound Nouns? (2024.naacl-short)
Copied to clipboard
| Challenge: | Open-vocabulary vision-language models (CLIP) are emerging as a promising new paradigm for text-to-image retrieval. |
| Approach: | They propose a benchmark to evaluate the effectiveness of open-vocabulary vision-language models (CLIP) for text-to-image retrieval using contrastive loss. |
| Outcome: | The proposed framework improves CN understanding of CLIP by 8.25% on Compun. |
Detecting cognitive impairments by agreeing on interpretations of linguistic features (N19-1)
Copied to clipboard
| Challenge: | Linguistic features have shown promising applications for detecting cognitive impairments. |
| Approach: | They propose a framework to classify after reaching agreements between modalities by using linguistic features to divide linguistic subsets into subset and let neural networks learn low-dimensional representations that agree with each other. |
| Outcome: | The proposed framework outperforms existing classifiers using all of the 413 linguistic features. |
Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation (2024.lrec-main)
Copied to clipboard
| Challenge: | Davidson et al.: hate speech is a growing media presence, but research on generating CNs has been limited . he says a new dataset for CN generation is available for basque and spanish . this dataset is based on a multilingual encoder-decoder model . |
| Approach: | They propose a new Basque and Spanish dataset for automatic CN generation . they use machine translation and professional post-edition to generate CNs in both languages . |
| Outcome: | The proposed datasets show that training on post-edited data improves generation over monolingual settings . similar results in zero-shot crosslingual evaluations show multilingual data augmentation outperforms training in English and Spanish . |
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study (2022.findings-acl)
Copied to clipboard
| Challenge: | Autoregressive models combined with stochastic decodings are the most promising for generating CNs with regard to an unseen target of hate. |
| Approach: | They propose to use pre-trained language models to generate counter-narratives in English by adding an automatic post-editing step to refine generated CNs. |
| Outcome: | The proposed pipeline could be used to generate counter-narratives in English using pre-trained language models and stochastic decoding mechanisms. |
CONAN-MT-SP: A Spanish Corpus for Counternarrative Using GPT Models (2024.lrec-main)
Copied to clipboard
María Estrella Vallecillo Rodríguez, Maria Victoria Cantero Romero, Isabel Cabrera De Castro, Arturo Montejo Ráez, María Teresa Martín Valdivia
| Challenge: | a new study evaluates the performance of GPT-based models to generate CNs for hate speech in Spanish . a growing number of social interactions through digital platforms have led to inappropriate behavior . |
| Approach: | They propose to use GPT-based models to generate CNs for Hate Speech in Spanish . they use the DeepL API to automatically translate the HS segment into Spanish based on the original CN pairs translated into spanish . |
| Outcome: | The proposed models outperform human models in most instances, the authors say . the results will be made available to the research community . |
Towards the Inference of Semantic Relations in Complex Nominals: a Pilot Study (L18-1)
Copied to clipboard
| Challenge: | Complex nominals (CNs) show similar external forms but encode different semantic relations because of noun packing. |
| Approach: | They propose to use paraphrases to convey conceptual content of english two-term CNs in the domain of environmental science to disambiguate the semantic relation between constituents of CN. |
| Outcome: | The proposed method disambiguates the semantic relation between constituents of the CN and infers the semantic relations in these multi-word terms. |
Contextualized Graph Representations for Generating Counter-Narratives against Hate Speech (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Hate speech (HS) is a widespread problem in society with severe repercussions at both personal and societal levels. |
| Approach: | They propose to incorporate conversational history into CNs to confront biases and stereotypes driving hateful narratives. |
| Outcome: | The proposed strategies outperform existing methods on comparing graphical and text representations with varying degrees of context. |