1 |
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
ExpMRC: explainability evaluation for machine reading comprehension
|
|
|
|
In: Heliyon (2022)
|
|
BASE
|
|
Show details
|
|
3 |
Multilingual multi-aspect explainability analyses on machine reading comprehension models
|
|
|
|
In: iScience (2022)
|
|
BASE
|
|
Show details
|
|
4 |
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Neural Stylistic Response Generation with Disentangled Latent Variables ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Language learners' enjoyment and emotion regulation in online collaborative learning
|
|
|
|
BASE
|
|
Show details
|
|
13 |
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Modeling Leadership Behavior of Players in Virtual Worlds
|
|
|
|
In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment; Vol. 11 No. 1 (2015): Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference ; 2334-0924 ; 2326-909X (2015)
|
|
Abstract:
In this article, we describe our method of modeling sociolinguistic behaviors of players in massively multi-player online games. The focus of this paper is leadership, as it is manifested by the participants engaged in discussion, and the automated modeling of this complex behavior in virtual worlds. We first approach the research question of modeling from a social science perspective, and ground our models in theories from human communication literature. We then adapt a two-tiered algorithmic model that derives certain mid-level sociolinguistic behaviors--such as Task Control, Topic Control and Disagreement from discourse linguistic indicators--and combines these in a weighted model to reveal the complex role of Leadership. The algorithm is evaluated by comparing its prediction of leaders against ground truth – the participants’ own ratings of leadership of themselves and their conversation peers. We find the algorithm performance to be considerably better than baseline.
|
|
Keyword:
automated models; computational; computational social science
|
|
URL: https://ojs.aaai.org/index.php/AIIDE/article/view/12844
|
|
BASE
|
|
Hide details
|
|
18 |
Question Popularity Analysis and Prediction in Community Question Answering Services
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Phrasal Paraphrase Based Question Reformulation for Archived Question Retrieval
|
|
|
|
BASE
|
|
Show details
|
|
|
|