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Found 11 papers, 4 papers with code
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues
We propose a shared task on summarizing real-life scenario dialogues, DialogSum Challenge, to encourage researchers to address challenges in dialogue summarization, which has been less studied by the summarization community.
Investigating Rich Feature Sources for Conceptual Representation Encoding
Functional Magnetic Resonance Imaging (fMRI) provides a means to investigate human conceptual representation in cognitive and neuroscience studies, where researchers predict the fMRI activations with elicited stimuli inputs.
The Cross-lingual Conversation Summarization Challenge
We propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation.
A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges
In this paper, to address the current lack of comprehensive survey of existing LJP tasks, datasets, models and evaluations, (1) we analyze 31 LJP datasets in 6 languages, present their construction process and define a classification method of LJP with 3 different attributes; (2) we summarize 14 evaluation metrics under four categories for different outputs of LJP tasks; (3) we review 12 legal-domain pretrained models in 3 languages and highlight 3 major research directions for LJP; (4) we show the state-of-art results for 8 representative datasets from different court cases and discuss the open challenges.
Graph Pre-training for AMR Parsing and Generation
To our knowledge, we are the first to consider pre-training on semantic graphs.
Ranked #1 on
AMR-to-Text Generation
on LDC2017T10
(using extra training data)
AdaPrompt: Adaptive Model Training for Prompt-based NLP
However, for prompt learning, there are still two salient gaps between NLP tasks and pretraining.
A Coarse-to-fine Morphological Approach With Knowledge-based Rules and Self-adapting Correction for Lung Nodules Segmentation
The segmentation module which precisely outlines the nodules is a crucial step in a computer-aided diagnosis(CAD) system.
MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detection
The false positive reduction module significantly decreases the average number of candidates generated per scan by 68. 11% and the false discovery rate by 13. 48%, which is promising to reduce distracted proposals for the downstream tasks based on the detection results.
On Compositional Generalization of Neural Machine Translation
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT.
Semantic Representation for Dialogue Modeling
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities.
Ranked #4 on
Dialog Relation Extraction
on DialogRE
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
Proposal of large-scale datasets has facilitated research on deep neural models for news summarization.
