Papers with comprehension tasks
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2023.emnlp-demo)
Copied to clipboard
| Challenge: | MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements. |
| Approach: | a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests . |
| Outcome: | the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements. |
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation (N19-1)
Copied to clipboard
| Challenge: | a study of long documents that do not include short sections in their titles shows that they improve comprehension and speed . |
| Approach: | They propose to extract the most salient sentence and apply deletion-based compression to generate section titles in low-resource environments. |
| Outcome: | The proposed approach outperforms other methods in low-resource environments while outperforming other approaches. |
The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge (2025.findings-naacl)
Copied to clipboard
| Challenge: | Sign language models could make language technologies more accessible to deaf and hard-of-hearing signers, but the supply of accurately labeled data struggles to meet the demand associated with training large, end-to-end architectures. |
| Approach: | They construct an American Sign Language Knowledge Graph from 11 sources of linguistic knowledge and use it to train neuro-symbolic models on ASL video input tasks. |
| Outcome: | The proposed model achieves 91% accuracies for isolated sign recognition, 14% for predicting the semantic features of unseen signs, and 36% for classifying the topic of Youtube-ASL videos. |
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension (2020.acl-main)
Copied to clipboard
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, Luke Zettlemoyer
| Challenge: | Recent work has shown gains by improving the distribution of masked tokens and the order in which mucked tokens are predicted. |
| Approach: | They propose a denoising autoencoder for pretraining sequence-to-sequence models that corrupts text with an arbitrary noising function and learns a model to reconstruct the original text. |
| Outcome: | The proposed model outperforms RoBERTa on GLUE and SQUAD and provides a 1.1 BLEU increase over a back-translation system for machine translation. |
BloomVQA: Assessing Hierarchical Multi-modal Comprehension (2024.findings-acl)
Copied to clipboard
Yunye Gong, Robik Shrestha, Jared Claypoole, Michael Cogswell, Arijit Ray, Christopher Kanan, Ajay Divakaran
| Challenge: | Recent advances of machine intelligence solutions have demonstrated tremendous success in a wide range of language and multi-modal tasks over diverse domains. |
| Approach: | They propose a VQA dataset to facilitate comprehensive evaluation of large vision-language models on comprehension tasks. |
| Outcome: | The proposed dataset shows improved accuracy over all comprehension levels and a tendency to bypass visual inputs especially for higher-level tasks. |