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Found 8 papers, 3 papers with code
STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension
Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks.
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning
Using human evaluation and automatic faithfulness metrics, we show that our model significantly reduces all kinds of factual errors on the dialogue summarization, SAMSum corpus.
KAT: A Knowledge Augmented Transformer for Vision-and-Language
The primary focus of recent work with largescale transformers has been on optimizing the amount of information packed into the model's parameters.
Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
Current pre-trained models applied to summarization are prone to factual inconsistencies which either misrepresent the source text or introduce extraneous information.
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.
Imitation Learning for Human Pose Prediction
Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of various architectures of recurrent neural networks.
Ranked #2 on
Human Pose Forecasting
on Human3.6M
(MAR, walking, 1,000ms metric)
Action-Agnostic Human Pose Forecasting
In this paper, we propose a new action-agnostic method for short- and long-term human pose forecasting.
Ranked #5 on
Human Pose Forecasting
on Human3.6M
(MAR, walking, 1,000ms metric)
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation
Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence.
