Papers with English portion
Triad-based Neural Network for Coreference Resolution (C18-1)
Copied to clipboard
| Challenge: | Entity coreference resolution aims to identify mentions that refer to the same entity. |
| Approach: | They propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. |
| Outcome: | The proposed system generates affinity scores between mentions for coreference resolution. |
SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects (2024.eacl-long)
Copied to clipboard
David Adelani, Hannah Liu, Xiaoyu Shen, Nikita Vassilyev, Jesujoba Alabi, Yanke Mao, Haonan Gao, En-Shiun Lee
| Challenge: | despite progress in building multilingual language models evaluation is limited to a few languages with available datasets . despite this, we create a large-scale open-sourced benchmark dataset for topic classification in 205 languages and dialects to address the lack of evaluation dataset for Natural Language Understanding (NLU). |
| Approach: | They create a large-scale open-sourced benchmark dataset for topic classification in 205 languages and dialects to address the lack of evaluation dataset for Natural Language Understanding (NLU). |
| Outcome: | The proposed dataset addresses the lack of evaluation dataset for Natural Language Understanding (NLU) for many languages, it is the first publicly available evaluation dataset. |
Sequence-to-Sequence Language Models for Character and Emotion Detection in Dream Narratives (2024.lrec-main)
Copied to clipboard
| Challenge: | Sigmund Freud's interpretation of dreams has been central to understanding human consciousness for centuries. |
| Approach: | They propose to automate the annotation process by using a natural language framework . they evaluate the impact of model size, prediction order of characters, and consideration of proper names and character traits . |
| Outcome: | The proposed model performs better with a large language model while having 28 times fewer parameters. |