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Found 46 papers, 14 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.
Syntax-guided Contrastive Learning for Pre-trained Language Model
Syntactic information has been proved to be useful for transformer-based pre-trained language models.
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification
To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision.
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion
This paper presents UNIMO-G, a simple multimodal conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs, which demonstrates a unified ability for both text-driven and subject-driven image generation.
UniVG: Towards UNIfied-modal Video Generation
This cannot fully meet the needs of real-world application scenarios, as users are likely to input images and text conditions in a flexible manner, either individually or in combination.
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM.
Test-Time Degradation Adaption for Open-Set Image Restoration
In contrast to close-set scenarios that restore images from a predefined set of degradations, open-set image restoration aims to handle the unknown degradations that were unforeseen during the pretraining phase, which is less-touched as far as we know.
UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning
The experimental results show that our model achieves state-of-the-art performance in various downstream tasks, and through ablation study can prove that effective cross-layer learning improves the model's ability of multimodal representation.
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
As a result, they perform poorly on the real generated text and are biased heavily by their single-source upstream tasks.
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information.
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance
Diffusion generative models have recently greatly improved the power of text-conditioned image generation.
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied.
SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset
As the first session-level Chinese dataset, CHASE contains two separate parts, i. e., 2, 003 sessions manually constructed from scratch (CHASE-C), and 3, 456 sessions translated from English SParC (CHASE-T).
An Interpretability Evaluation Benchmark for Pre-trained Language Models
To fill in the gap, we propose a novel evaluation benchmark providing with both English and Chinese annotated data.
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP
Based on this benchmark, we conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability.
Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss
Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries.
Unified Structure Generation for Universal Information Extraction
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on ASTE
(using extra training data)
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.
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.
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.
Learning with Noisy Correspondence for Cross-modal Matching
Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.
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.
A Multimodal Sentiment Dataset for Video Recommendation
Based on this, we propose a multimodal sentiment analysis dataset, named baiDu Video Sentiment dataset (DuVideoSenti), and introduce a new sentiment system which is designed to describe the sentimental style of a video on recommendation scenery.
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.
Fine-grained Entity Typing via Label Reasoning
Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types.
Ranked #8 on
Entity Typing
on Open Entity
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation
Therefore, in order to systematically evaluate the factors for building trustworthy systems, we propose a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability.
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.
Weakly Supervised Dense Video Captioning via Jointly Usage of Knowledge Distillation and Cross-modal Matching
This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation.
Syntactic and Semantic-driven Learning for Open Information Extraction
One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora.
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing
For better distribution matching, we require that at least 80% of SQL patterns in the training data are covered by generated queries.
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 #3 on
Image Captioning
on MS COCO
A Practical Chinese Dependency Parser Based on A Large-scale Dataset
DDParser is extended on the graph-based biaffine parser to accommodate to the characteristics of Chinese dataset.
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.
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
Ranked #14 on
Stock Market Prediction
on Astock
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification
For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling.
Joint extraction of entities and overlapping relations using position-attentive sequence labeling
Joint entity and relation extraction is to detect entity and relation using a single model.
Ranked #2 on
Relation Extraction
on NYT-single
ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification
In this paper, we propose ARNOR, a novel Attention Regularization based NOise Reduction framework for distant supervision relation classification.
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling
Information selection is the most important component in document summarization task.
Ranked #32 on
Abstractive Text Summarization
on CNN / Daily Mail
Improving Neural Abstractive Document Summarization with Structural Regularization
Recent neural sequence-to-sequence models have shown significant progress on short text summarization.
Ranked #43 on
Abstractive Text Summarization
on CNN / Daily Mail
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.
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
