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Found 12 papers, 8 papers with code
Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze
Improving factual consistency in abstractive summarization has been a focus of current research.
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme.
Pre-training Multi-party Dialogue Models with Latent Discourse Inference
Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows.
EM Pre-training for Multi-party Dialogue Response Generation
Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time.
Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins
We consider the problem of correct motion planning for T-intersection merge-ins of arbitrary geometry and vehicle density.
Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization
The issue of factual consistency in abstractive summarization has received extensive attention in recent years, and the evaluation of factual consistency between summary and document has become an important and urgent task.
Semantic-Preserving Adversarial Code Comprehension
Based on the tremendous success of pre-trained language models (PrLMs) for source code comprehension tasks, current literature studies either ways to further improve the performance (generalization) of PrLMs, or their robustness against adversarial attacks.
Motion Prediction for Beating Heart Surgery with GRU
This work aims to predict the 3D coordinates of the point of interest (POI) on the surface of beating heart in dynamic minimally invasive surgery, which can improve the manoeuvrability of cardiac surgical robots and expand their functions.
Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling
Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provides a solid foundation for multiple downstream tasks.
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension
Multi-party dialogue machine reading comprehension (MRC) brings tremendous challenge since it involves multiple speakers at one dialogue, resulting in intricate speaker information flows and noisy dialogue contexts.
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Recurrent multiple shared layers in Depth for Neural Machine Translation
Compared to the deep Transformer(20-layer encoder, 6-layer decoder), our model has similar model performance and infer speed, but our model parameters are 54. 72% of the former.
Residual Tree Aggregation of Layers for Neural Machine Translation
In transformer, it only uses the top layer of encoder and decoder in the subsequent process, which makes it impossible to take advantage of the useful information in other layers.
