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Found 16 papers, 7 papers with code
SgSum:Transforming Multi-document Summarization into Sub-graph Selection
Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent.
End-to-end Graph-constrained Vectorized Floorplan Generation with Panoptic Refinement
In the first stage, we encode the room connectivity graph input by users with a graph convolutional network (GCN), then apply an autoregressive transformer network to generate an initial floorplan sequence.
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
The semantic plane detection branch is based on a single-view plane detection framework but with differences.
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow.
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training
To bridge the gap between image understanding and generation, we further design a novel commitment loss.
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning
In particular, we propose to conduct grounded learning on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the visual and textual semantic spaces on different types of corpora.
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
In this survey, we provide a systematic overview of the research progress on the faithfulness problem of NLG, including problem analysis, evaluation metrics and optimization methods.
SgSum: Transforming Multi-document Summarization into Sub-graph Selection
Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent.
Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward
Controllable text generation is an appealing but challenging task, which allows users to specify particular attributes of the generated outputs.
BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research.
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other.
Ranked #2 on
Image Captioning
on COCO
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries.
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data.
An Empirical Study of Propagation-based Methods for Video Object Segmentation
While propagation-based approaches have achieved state-of-the-art performance for video object segmentation, the literature lacks a fair comparison of different methods using the same settings.
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension
While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates.
