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Found 81 papers, 41 papers with code
Multi-task Learning with Gradient Communication
In this paper, we describe a general framework to systematically analyze current neural models for multi-task learning, in which we find that existing models expect to disentangle features into different spaces while features learned in practice are still entangled in shared space, leaving potential hazards for other training or unseen tasks.
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization
In this paper, we present an adversarial meta-evaluation methodology that allows us to (i) diagnose the fine-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets, (ii) search for directions for further improvement by data augmentation.
Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation
Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics.
GPTScore: Evaluate as You Desire
Generative Artificial Intelligence (AI) has enabled the development of sophisticated models that are capable of producing high-caliber text, images, and other outputs through the utilization of large pre-trained models.
a cognitive frequency allocation strategy for multi-carrier radar against communication interference
To address this challenge, we use a novel iteratively selecting technique which breaks a difficult decision task into several easy tasks.
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation
4) We evaluate existing automatic metrics using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results.
T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics
Both have their advantages; discriminative metrics are able to directly optimize for the problem of distinguishing between good and bad outputs, while generative metrics can be trained using abundant raw text.
Searching for Effective Multilingual Fine-Tuning Methods: A Case Study in Summarization
Recently, a large number of tuning strategies have been proposed to adapt pre-trained language models to downstream tasks.
PAL: Program-aided Language Models
Much of this success can be attributed to prompting methods such as "chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem.
Towards a Unified Multi-Dimensional Evaluator for Text Generation
We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.
Artificial Neural Networks for Finger Vein Recognition: A Survey
To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples.
reStructured Pre-training
In addition, we test our model in the 2022 College Entrance Examination English that happened a few days ago (2022. 06. 08), and it gets a total score of 134 (v. s.
I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation
In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation.
Ranked #1 on
Pose Estimation
on COCO
Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification
In this paper, we ask the research question of whether all the datasets in the benchmark are necessary.
Polyglot Prompt: Multilingual Multitask PrompTraining
This paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i. e. without any task/language-specific module?
BRIO: Bringing Order to Abstractive Summarization
Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary.
Ranked #2 on
Text Summarization
on X-Sum
DataLab: A Platform for Data Analysis and Intervention
Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data.
The MSXF TTS System for ICASSP 2022 ADD Challenge
The end to end TTS system is VITS, and the pre-training self-supervised model is wav2vec 2. 0.
Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition
Emotion recognition is a challenging and actively-studied research area that plays a critical role in emotion-aware human-computer interaction systems.
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning".
BARTScore: Evaluating Generated Text as Text Generation
In this work, we conceptualize the evaluation of generated text as a text generation problem, modeled using pre-trained sequence-to-sequence models.
How well do you know your summarization datasets?
(2) The performance of models and reliability of metrics is dependent on sample complexity.
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction
Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress.
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i. e., quality estimation) assisted by contrastive learning.
Ranked #4 on
Text Summarization
on X-Sum
SpanNER: Named Entity Re-/Recognition as Span Prediction
Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction.
Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling
User queries for a real-world dialog system may sometimes fall outside the scope of the system's capabilities, but appropriate system responses will enable smooth processing throughout the human-computer interaction.
Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing
This paper presents an unsupervised two-stage approach to discover intents and generate meaningful intent labels automatically from a collection of unlabeled utterances in a domain.
RefSum: Refactoring Neural Summarization
Although some recent works show potential complementarity among different state-of-the-art systems, few works try to investigate this problem in text summarization.
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others.
ExplainaBoard: An Explainable Leaderboard for NLP
In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e. g.~what is the best-performing system bad at?)
Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa
In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree.
Larger-Context Tagging: When and Why Does It Work?
The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks.
Towards More Fine-grained and Reliable NLP Performance Prediction
We perform an analysis of four types of NLP tasks, and both demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future.
Can We Automate Scientific Reviewing?
The rapid development of science and technology has been accompanied by an exponential growth in peer-reviewed scientific publications.
Polymorphous density-functional description of paramagnetic phases of quantum magnets
The kernel of the study of magnetic quantum materials focuses on the magnetic phase transitions, among which the most common phenomenon is the transition between low-temperature magnetic-ordered phase to high-temperature paramagnetic phase.
Materials Science
Interpretable Multi-dataset Evaluation for Named Entity Recognition
With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits.
RethinkCWS: Is Chinese Word Segmentation a Solved Task?
The performance of the Chinese Word Segmentation (CWS) systems has gradually reached a plateau with the rapid development of deep neural networks, especially the successful use of large pre-trained models.
Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics
In text summarization, evaluating the efficacy of automatic metrics without human judgments has become recently popular.
GSum: A General Framework for Guided Neural Abstractive Summarization
Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control.
Re-evaluating Evaluation in Text Summarization
Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization.
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora.
Heterogeneous Graph Neural Networks for Extractive Document Summarization
An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships.
Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis
We construct a Median-of-Means (MOM) estimator for the centered log-ratio covariance matrix and propose a thresholding procedure that is adaptive to the variability of individual entries.
Methodology
Extractive Summarization as Text Matching
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.
Ranked #1 on
Text Summarization
on BBC XSum
Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study
While neural network-based models have achieved impressive performance on a large body of NLP tasks, the generalization behavior of different models remains poorly understood: Does this excellent performance imply a perfect generalization model, or are there still some limitations?
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space.
Ranked #6 on
Vehicle Pose Estimation
on KITTI Cars Hard
Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning
In this paper, a decentralized spectrum allocation approach is presented to avoid mutual interference among automotive radars.
Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis
Second, it shows a strong advantage in determining the sentiment of a target when the context sentence contains multiple semantic segments.
Multi-Scale Self-Attention for Text Classification
In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules.
Learning Sparse Sharing Architectures for Multiple Tasks
Most existing deep multi-task learning models are based on parameter sharing, such as hard sharing, hierarchical sharing, and soft sharing.
A Closer Look at Data Bias in Neural Extractive Summarization Models
In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models.
A TWO-STAGE FRAMEWORK FOR MATHEMATICAL EXPRESSION RECOGNITION
In the first stage, this method locates and recognizes the math symbols of input image by object detection algorithm.
Towards Interpretable Evaluations: A Case Study of Named Entity Recognition
With the proliferation of models for natural language processing (NLP) tasks, it is even harder to understand the differences between models and their relative merits.
Exploring Domain Shift in Extractive Text Summarization
Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization.
Zero-shot Text-to-SQL Learning with Auxiliary Task
Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task.
DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks
Variants dropout methods have been designed for the fully-connected layer, convolutional layer and recurrent layer in neural networks, and shown to be effective to avoid overfitting.
Searching for Effective Neural Extractive Summarization: What Works and What's Next
The recent years have seen remarkable success in the use of deep neural networks on text summarization.
Ranked #6 on
Extractive Text Summarization
on CNN / Daily Mail
TIGS: An Inference Algorithm for Text Infilling with Gradient Search
Text infilling is defined as a task for filling in the missing part of a sentence or paragraph, which is suitable for many real-world natural language generation scenarios.
Cognitive Radar Using Reinforcement Learning in Automotive Applications
The concept of cognitive radar (CR) enables radar systems to achieve intelligent adaption to a changeable environment with feedback facility from receiver to transmitter.
Star-Transformer
Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data.
Ranked #12 on
Sentiment Analysis
on SST-5 Fine-grained classification
Named Entity Recognition (NER)
Natural Language Inference
+2
Drug cell line interaction prediction
Understanding the phenotypic drug response on cancer cell lines plays a vital rule in anti-cancer drug discovery and re-purposing.
Multi-task Learning over Graph Structures
We present two architectures for multi-task learning with neural sequence models.
Contextualized Non-local Neural Networks for Sequence Learning
Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which self-attention, as exemplified by the Transformer model, and graph neural networks (GNNs) have attracted much attention.
Meta-Learning Multi-task Communication
In this paper, we describe a general framework: Parameters Read-Write Networks (PRaWNs) to systematically analyze current neural models for multi-task learning, in which we find that existing models expect to disentangle features into different spaces while features learned in practice are still entangled in shared space, leaving potential hazards for other training or unseen tasks.
Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network
Effective representation of a text is critical for various natural language processing tasks.
Meta Multi-Task Learning for Sequence Modeling
Specifically, we use a shared meta-network to capture the meta-knowledge of semantic composition and generate the parameters of the task-specific semantic composition models.
Idiom-Aware Compositional Distributed Semantics
Idioms are peculiar linguistic constructions that impose great challenges for representing the semantics of language, especially in current prevailing end-to-end neural models, which assume that the semantics of a phrase or sentence can be literally composed from its constitutive words.
Dynamic Compositional Neural Networks over Tree Structure
Tree-structured neural networks have proven to be effective in learning semantic representations by exploiting syntactic information.
Adversarial Multi-task Learning for Text Classification
Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features.
Deep Multi-Task Learning with Shared Memory
Neural network based models have achieved impressive results on various specific tasks.
Syntax-based Attention Model for Natural Language Inference
Introducing attentional mechanism in neural network is a powerful concept, and has achieved impressive results in many natural language processing tasks.
Modelling Interaction of Sentence Pair with coupled-LSTMs
Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks.
Ranked #73 on
Natural Language Inference
on SNLI
Recurrent Neural Network for Text Classification with Multi-Task Learning
Neural network based methods have obtained great progress on a variety of natural language processing tasks.
Ranked #10 on
Emotion Recognition in Conversation
on CPED
Emotion Recognition in Conversation
General Classification
+3
