Papers with modeling methods
A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News (2024.lrec-main)
Copied to clipboard
| Challenge: | a new dataset is being developed to enrich resources for sign language research . the dataset is 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words. |
| Approach: | They introduce a new Hong Kong sign language dataset called TVB-HKSL-News . the dataset is collected from a TV news program and contains sign videos . they aim to support research in sign language recognition and translation . |
| Outcome: | The proposed dataset supports sign language recognition and translation research in Hong Kong . it consists of 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words . |
High-order Semantic Role Labeling (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that high-order structural learning techniques are beneficial to SRL models . high-level features and structure learning are not common in deep neural networks . |
| Approach: | They propose a high-order graph structure for a neural semantic role labeling model . it explicitly considers the isolated predicate-argument pairs and interaction between them . |
| Outcome: | The proposed model can explicitly consider the isolated predicate-argument pairs and the interaction between the predicates-argoments pairs. |
Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use (2026.findings-eacl)
Copied to clipboard
Yiyang Li, Zehong Wang, Zhengqing Yuan, Zheyuan Zhang, Keerthiram Murugesan, Chuxu Zhang, Yanfang Ye
| Challenge: | Illicit drug use among teens and young adults remains a public health concern . existing models ignore latent and interconnected structures among survey variables . |
| Approach: | They propose a joint graph-language modeling framework to detect illicit drug use among TYAs . they use large-scale surveys such as the Youth Risk Behavior Survey and the National Survey on Drug Use and Health to analyze data . |
| Outcome: | The proposed framework outperforms baseline models on YRBS and NSDUH datasets in predictive accuracy. |
Executing Instructions in Situated Collaborative Interactions (D19-1)
Copied to clipboard
Alane Suhr, Claudia Yan, Jack Schluger, Stanley Yu, Hadi Khader, Marwa Mouallem, Iris Zhang, Yoav Artzi
| Challenge: | a collaborative game with natural language instruction allows users to adapt to the system abilities by changing their language or deciding to accomplish tasks themselves. |
| Approach: | They propose a collaborative game where a user instructs a system to complete tasks, but acts alongside it. |
| Outcome: | The proposed game allows users to adapt to the system abilities by changing their language or deciding to accomplish tasks themselves. |
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning (2022.coling-1)
Copied to clipboard
| Challenge: | e-commerce has become a research hotspot for review helpfulness prediction . a new approach to help predict helpfulness of multimodal product reviews is proposed . |
| Approach: | They propose a machine learning task to identify helpfulness of multimodal product reviews . they use a probe-based strategy to enforce high attention weights on regions of greater significance . |
| Outcome: | The proposed model achieves state-of-the-art performance with lower memory consumption on two benchmark datasets with three categories. |
Revisiting Over-Smoothness in Text to Speech (2022.acl-long)
Copied to clipboard
| Challenge: | Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results. |
| Approach: | They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods . |
| Outcome: | The proposed models achieve better voice quality and faster inference speed than autoregressive models. |
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks (2023.acl-long)
Copied to clipboard
| Challenge: | Neural based end-to-end frameworks have achieved remarkable success in speech-totext tasks, such as automatic speech recognition (ASR) and speech- totext translation (ST). |
| Approach: | They propose to combine Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks and leverage AED's strength in non-monotonic sequence to sequence learning while retaining Transducers streaming property. |
| Outcome: | The proposed model outperforms Transducer and Attention based Encoder-Decoder (TAED) on the MuST-C dataset and shows that it is not bound by any specific language model. |