Search Results for author:
Found 163 papers, 83 papers with code
Re-examining the Role of Schema Linking in Text-to-SQL
By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking.
APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning
Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments.
Ranked #3 on
Argument Pair Extraction (APE)
on RR
SmartCiteCon: Implicit Citation Context Extraction from Academic Literature Using Supervised Learning
The tool is built on a Support Vector Machine (SVM) model trained on a set of 7, 058 manually annotated citation context sentences, curated from 34, 000 papers from the ACL Anthology.
SailCompass: Towards Reproducible and Robust Evaluation for Southeast Asian Languages
In this paper, we introduce SailCompass, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA).
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual Tasks
However, in remote sensing (RS) imagery, where small and multiscale object detection is critical, these networks often fall short in performance.
Ranked #2 on
Image Classification
on RESISC45
ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification
The misuse of deep learning-based facial manipulation poses a potential threat to civil rights.
PersonaMark: Personalized LLM watermarking for model protection and user attribution
Specifically, we propose a novel text watermarking method PersonaMark that utilizes sentence structure as the hidden medium for the watermark information and optimizes the sentence-level generation algorithm to minimize disruption to the model's natural generation process.
Self-Harmonized Chain of Thought
Chain-of-Thought (CoT) prompting reveals that large language models are capable of performing complex reasoning via intermediate steps.
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities
The advancement of Large Language Models (LLMs) for domain applications in fields such as materials science and engineering depends on the development of fine-tuning strategies that adapt models for specialized, technical capabilities.
FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting
Through extensive evaluations, we show FRAP generates images with significantly higher prompt-image alignment to prompts from complex datasets, while having a lower average latency compared to recent latent code optimization methods, e. g., 4 seconds faster than D&B on the COCO-Subject dataset.
Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making
The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation.
Every Part Matters: Integrity Verification of Scientific Figures Based on Multimodal Large Language Models
We introduce a novel task, Figure Integrity Verification, designed to evaluate the precision of technologies in aligning textual knowledge with visual elements in scientific figures.
Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning
We also show that the conventional self-training can be further augmented by a preference learning algorithm called Direct Preference Optimization (DPO).
Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization
Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio.
Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
Retrieval-Augmented Generation (RAG) is applied to solve hallucination problems and real-time constraints of large language models, but it also induces vulnerabilities against retrieval corruption attacks.
Rapid and Accurate Diagnosis of Acute Aortic Syndrome using Non-contrast CT: A Large-scale, Retrospective, Multi-center and AI-based Study
Our AI model performed well on non-contrast CT at all applicable early stages of differential diagnosis workflows, effectively reduced the overall missed diagnosis and misdiagnosis rate from 48. 8% to 4. 8% and shortened the diagnosis time for patients with misguided initial suspicion from an average of 681. 8 (74-11, 820) mins to 68. 5 (23-195) mins.
A Full Adagrad algorithm with O(Nd) operations
A novel approach is given to overcome the computational challenges of the full-matrix Adaptive Gradient algorithm (Full AdaGrad) in stochastic optimization.
G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction
Finally, to ensure the optimal granularity of key steps, we design a selectable granularity strategy that caters to each predicted trajectory.
Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
This study conducts a comprehensive review and analysis of the existing literature on the attitudes of LLMs towards the 17 SDGs, emphasizing the comparison between their attitudes and support for each goal and those of humans.
Enhance Robustness of Language Models Against Variation Attack through Graph Integration
The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes.
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications
Therefore, our study shifts the focus from model-centered to human-centered evaluation in the context of AI-powered writing assistance applications.
Sailor: Open Language Models for South-East Asia
We present Sailor, a family of open language models ranging from 0. 5B to 7B parameters, tailored for South-East Asian (SEA) languages.
Constrained Layout Generation with Factor Graphs
This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process.
Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation
As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable challenges, particularly in cases of unauthorized reuse of generated data.
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs).
Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning
Demonstration ordering, which is an important strategy for in-context learning (ICL), can significantly affects the performance of large language models (LLMs).
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology
A stable, reliable, and controllable orbit lock system is crucial to an electron (or ion) accelerator because the beam orbit and beam energy instability strongly affect the quality of the beam delivered to experimental halls.
Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
This paper addresses second-order stochastic optimization for estimating the minimizer of a convex function written as an expectation.
TinyLlama: An Open-Source Small Language Model
We present TinyLlama, a compact 1. 1B language model pretrained on around 1 trillion tokens for approximately 3 epochs.
Tell2Design: A Dataset for Language-Guided Floor Plan Generation
We make multiple contributions to initiate research on this task.
Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement
In contrast, we propose distinguishing effects derived from various factors and predicting citation increments as estimated potential impacts within the dynamic context.
OptScaler: A Hybrid Proactive-Reactive Framework for Robust Autoscaling in the Cloud
The reactive module provides a self-tuning estimator of CPU utilization to the optimization model.
Unraveling Feature Extraction Mechanisms in Neural Networks
The underlying mechanism of neural networks in capturing precise knowledge has been the subject of consistent research efforts.
Tuna: Instruction Tuning using Feedback from Large Language Models
Furthermore, we apply probabilistic ranking and contextual ranking sequentially to the instruction-tuned LLM.
Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher
The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches.
Decomposed Prompt Tuning via Low-Rank Reparameterization
While prompt tuning approaches have achieved competitive performance with high efficiency, we observe that they invariably employ the same initialization process, wherein the soft prompt is either randomly initialized or derived from an existing embedding vocabulary.
Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning
Therefore, we shift the attention to the current task learning stage, presenting a novel framework, C&F (Create and Find Flatness), which builds a flat training space for each task in advance.
Learning Multi-Step Reasoning by Solving Arithmetic Tasks
Mathematical reasoning is regarded as a necessary ability for Language Models (LMs).
Ranked #2 on
Math Word Problem Solving
on MAWPS
A Robust Feature Downsampling Module for Remote Sensing Visual Tasks
To address this problem, we propose a new and universal downsampling module named robust feature downsampling (RFD).
Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers
To leverage this knowledge, we propose a novel chart-based method for extracting parse trees from masked language models (LMs) without the need to train separate parsers.
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning
To address these issues, we investigate two approaches to leverage the training data in a few-shot prompting scenario: dynamic program prompting and program distillation.
Tab-CoT: Zero-shot Tabular Chain of Thought
The chain-of-though (CoT) prompting methods were successful in various natural language processing (NLP) tasks thanks to their ability to unveil the underlying complex reasoning processes.
One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning
Fine-tuning pre-trained language models for multiple tasks tends to be expensive in terms of storage.
Better Sampling of Negatives for Distantly Supervised Named Entity Recognition
Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data instead of human annotations.
CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition
Emotion recognition using Electroencephalogram (EEG) signals has emerged as a significant research challenge in affective computing and intelligent interaction.
Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge
Inspired by recent advancement in prompt learning, in this paper, we propose the Mix Prompt Tuning (MPT), which is a semi-supervised method to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks with a small number of labeled examples.
H2CGL: Modeling Dynamics of Citation Network for Impact Prediction
Then, a novel graph neural network, Hierarchical and Heterogeneous Contrastive Graph Learning Model (H2CGL), is proposed to incorporate heterogeneity and dynamics of the citation network.
Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing
Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e. g., lightweight but high strength, achieving diverse mechanical responses).
Online stochastic Newton methods for estimating the geometric median and applications
In the context of large samples, a small number of individuals might spoil basic statistical indicators like the mean.
MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos
User-generated content (UGC) live videos are often bothered by various distortions during capture procedures and thus exhibit diverse visual qualities.
A General-Purpose Transferable Predictor for Neural Architecture Search
In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators.
EEP-3DQA: Efficient and Effective Projection-based 3D Model Quality Assessment
Currently, great numbers of efforts have been put into improving the effectiveness of 3D model quality assessment (3DQA) methods.
AI vs. Human -- Differentiation Analysis of Scientific Content Generation
We find that there exists a "writing style" gap between AI-generated scientific text and human-written scientific text.
DDH-QA: A Dynamic Digital Humans Quality Assessment Database
In recent years, large amounts of effort have been put into pushing forward the real-world application of dynamic digital human (DDH).
Named Entity and Relation Extraction with Multi-Modal Retrieval
MoRe contains a text retrieval module and an image-based retrieval module, which retrieve related knowledge of the input text and image in the knowledge corpus respectively.
Ranked #1 on
Multi-modal Named Entity Recognition
on SNAP (MNER)
Multi-modal Named Entity Recognition
Named Entity Recognition
+3
AIO-P: Expanding Neural Performance Predictors Beyond Image Classification
Evaluating neural network performance is critical to deep neural network design but a costly procedure.
GENNAPE: Towards Generalized Neural Architecture Performance Estimators
In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and fuzzy clustering-based predictor ensemble.
Differentiable Data Augmentation for Contrastive Sentence Representation Learning
Fine-tuning a pre-trained language model via the contrastive learning framework with a large amount of unlabeled sentences or labeled sentence pairs is a common way to obtain high-quality sentence representations.
Better Few-Shot Relation Extraction with Label Prompt Dropout
Our experiments show that our approach is able to lead to improved class representations, yielding significantly better results on the few-shot relation extraction task.
Unsupervised Non-transferable Text Classification
Training a good deep learning model requires substantial data and computing resources, which makes the resulting neural model a valuable intellectual property.
Generative Prompt Tuning for Relation Classification
Current prompt tuning methods mostly convert the downstream tasks to masked language modeling problems by adding cloze-style phrases and mapping all labels to verbalizations with fixed length, which has proven effective for tasks with simple label spaces.
Ranked #2 on
Relation Extraction
on Re-TACRED
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models
In this study, we propose an imitation adversarial attack on black-box neural passage ranking models.
Subjective Quality Assessment for Images Generated by Computer Graphics
However, limited work has been put forward to tackle the problem of computer graphics generated images' quality assessment (CG-IQA).
A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences
Point cloud is one of the most widely used digital formats of 3D models, the visual quality of which is quite sensitive to distortions such as downsampling, noise, and compression.
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps
Therefore, in this paper, we propose a no-reference deep-learning image quality assessment method based on frequency maps because the artifacts caused by SISR algorithms are quite sensitive to frequency information.
Deep Neural Network for Blind Visual Quality Assessment of 4K Content
In this paper, we propose a deep learning-based BIQA model for 4K content, which on one hand can recognize true and pseudo 4K content and on the other hand can evaluate their perceptual visual quality.
Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency
In this paper, we first conduct an example experiment (i. e. the face detection task) to demonstrate that the quality of the SIs has a crucial impact on the performance of the IVSS, and then propose a saliency-based deep neural network for the blind quality assessment of the SIs, which helps IVSS to filter the low-quality SIs and improve the detection and recognition performance.
Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior
In the asymmetric codec, we adopt a mixed multi-path residual block (MMRB) to gradually extract weak texture features of input images, which can better preserve the original facial features and avoid excessive fantasy.
ADT-SSL: Adaptive Dual-Threshold for Semi-Supervised Learning
Semi-Supervised Learning (SSL) has advanced classification tasks by inputting both labeled and unlabeled data to train a model jointly.
Implicit N-grams Induced by Recurrence
Although self-attention based models such as Transformers have achieved remarkable successes on natural language processing (NLP) tasks, recent studies reveal that they have limitations on modeling sequential transformations (Hahn, 2020), which may prompt re-examinations of recurrent neural networks (RNNs) that demonstrated impressive results on handling sequential data.
A Deep Learning based No-reference Quality Assessment Model for UGC Videos
The proposed model utilizes very sparse frames to extract spatial features and dense frames (i. e. the video chunk) with a very low spatial resolution to extract motion features, which thereby has low computational complexity.
Ranked #5 on
Video Quality Assessment
on YouTube-UGC
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction
Solving math word problems requires deductive reasoning over the quantities in the text.
Ranked #4 on
Math Word Problem Solving
on MathQA
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Multilingual Named Entity Recognition
Named Entity Recognition
+1
LEARNING GUARANTEES FOR GRAPH CONVOLUTIONAL NETWORKS ON THE STOCHASTIC BLOCK MODEL
However, very few provable guarantees have been available for the performance of graph neural network models.
A Transferable General-Purpose Predictor for Neural Architecture Search
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS).
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications.
L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
In this paper, we propose L$^{2}$NAS, which learns to intelligently optimize and update architecture hyperparameters via an actor neural network based on the distribution of high-performing architectures in the search history.
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis
Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension.
To be Closer: Learning to Link up Aspects with Opinions
Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Exploring Task Difficulty for Few-Shot Relation Extraction
Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances.
Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction
Dialogue-based relation extraction (DiaRE) aims to detect the structural information from unstructured utterances in dialogues.
Uncovering Main Causalities for Long-tailed Information Extraction
Information Extraction (IE) aims to extract structural information from unstructured texts.
No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models
Therefore, many related studies such as point cloud quality assessment (PCQA) and mesh quality assessment (MQA) have been carried out to measure the visual quality degradations of 3D models.
Ranked #3 on
Point Cloud Quality Assessment
on WPC
Mixed Cross Entropy Loss for Neural Machine Translation
In neural machine translation, cross entropy (CE) is the standard loss function in two training methods of auto-regressive models, i. e., teacher forcing and scheduled sampling.
Detection of Deepfake Videos Using Long Distance Attention
And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively.
Interventional Video Grounding with Dual Contrastive Learning
2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations.
Generative Adversarial Neural Architecture Search
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess.
A parallel-network continuous quantitative trading model with GARCH and PPO
It is a difficult task for both professional investors and individual traders continuously making profit in stock market.
Better Feature Integration for Named Entity Recognition
We believe this is because both types of features - the contextual information captured by the linear sequences and the structured information captured by the dependency trees may complement each other.
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations
Our work is the first to study disentanglement learning for graph-level representations.
Counterfactual Thinking for Long-tailed Information Extraction
However, in practice, the long-tailed and imbalanced data may lead to severe bias issues for deep learning models, due to very few training instances available for the tail classes.
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS
Based on the closed-form approximations of the hidden states, we argue that the effectiveness of the cells may be attributed to a type of sequence-level representations brought in by the gating mechanism, which enables the cells to encode sequence-level features along with token-level features.
Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff
Is chatbot able to completely replace the human agent?
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity.
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
In this work, we propose the novel {\em table-sequence encoders} where two different encoders -- a table encoder and a sequence encoder are designed to help each other in the representation learning process.
Ranked #2 on
Zero-shot Relation Triplet Extraction
on Wiki-ZSL
Joint Entity and Relation Extraction
named-entity-recognition
+5
Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans
Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features.
Position-Aware Tagging for Aspect Sentiment Triplet Extraction
Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach.
Ranked #3 on
Aspect Sentiment Triplet Extraction
on SemEval
RPT: Learning Point Set Representation for Siamese Visual Tracking
While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem.
Ranked #13 on
Semi-Supervised Video Object Segmentation
on VOT2020
Understanding Attention for Text Classification
Attention has been proven successful in many natural language processing (NLP) tasks.
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities.
Ranked #9 on
Relation Extraction
on GDA
WN-Salience: A Corpus of News Articles with Entity Salience Annotations
To support research on entity salience, we present a new dataset, the WikiNews Salience dataset (WN-Salience), which can be used to benchmark tasks such as entity salience detection and salient entity linking.
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description.
Ranked #1 on
KG-to-Text Generation
on ENT-DESC
Pre-training for Abstractive Document Summarization by Reinstating Source Text
The main idea is that, given an input text artificially constructed from a document, a model is pre-trained to reinstate the original document.
Adversarial Deep Network Embedding for Cross-network Node Classification
This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information.
Mining Commonsense Facts from the Physical World
In this paper, we propose a new task of mining commonsense facts from the raw text that describes the physical world.
Self-Directed Online Machine Learning for Topology Optimization
A small number of training data is generated dynamically based on the DNN's prediction of the optimum.
Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model
The nonliteral interpretation of a text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language.
Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Ranked #5 on
Aspect Sentiment Triplet Extraction
on SemEval
Text2Math: End-to-end Parsing Text into Math Expressions
We propose Text2Math, a model for semantically parsing text into math expressions.
Aligning Cross-Lingual Entities with Multi-Aspect Information
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages.
Dependency-Guided LSTM-CRF for Named Entity Recognition
Dependency tree structures capture long-distance and syntactic relationships between words in a sentence.
Ranked #1 on
Chinese Named Entity Recognition
on OntoNotes 5.0
Chinese Named Entity Recognition
named-entity-recognition
+2
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis
In this work, we argue that both types of information (implicit and explicit structural information) are crucial for building a successful targeted sentiment analysis model.
UER: An Open-Source Toolkit for Pre-training Models
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks.
Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities
In medical documents, it is possible that an entity of interest not only contains a discontiguous sequence of words but also overlaps with another entity.
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems
An arithmetic word problem typically includes a textual description containing several constant quantities.
Joint Detection and Location of English Puns
A pun is a form of wordplay for an intended humorous or rhetorical effect, where a word suggests two or more meanings by exploiting polysemy (homographic pun) or phonological similarity to another word (heterographic pun).
The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy
Index Terms-Fuzzy cognitive maps (FCMs), maximum entropy, noisy data, rapid and robust learning.
Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation.
A Neural Multi-digraph Model for Chinese NER with Gazetteers
Gazetteers were shown to be useful resources for named entity recognition (NER).
Twitter Homophily: Network Based Prediction of User's Occupation
In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user{'}s occupational class.
Attention Guided Graph Convolutional Networks for Relation Extraction
Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text.
Ranked #26 on
Relation Extraction
on TACRED
Neural Chinese Address Parsing
This paper introduces a new task {--} Chinese address parsing {--} the task of mapping Chinese addresses into semantically meaningful chunks.
Better Modeling of Incomplete Annotations for Named Entity Recognition
Supervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information.
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
Learning word embeddings has received a significant amount of attention recently.
Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators
We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences.
Efficient Dependency-Guided Named Entity Recognition
It has been shown previously that such information can be used to improve the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012).
Weak Semi-Markov CRFs for NP Chunking in Informal Text
This paper introduces a new annotated corpus based on an existing informal text corpus: the NUS SMS Corpus (Chen and Kan, 2013).
Learning to Recognize Discontiguous Entities
This paper focuses on the study of recognizing discontiguous entities.
Neural Adaptation Layers for Cross-domain Named Entity Recognition
Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets.
A Neural Transition-based Model for Nested Mention Recognition
It is common that entity mentions can contain other mentions recursively.
Ranked #6 on
Nested Named Entity Recognition
on NNE
Nested Mention Recognition
Nested Named Entity Recognition
+1
Neural Segmental Hypergraphs for Overlapping Mention Recognition
In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets.
Ranked #5 on
Nested Named Entity Recognition
on NNE
Nested Mention Recognition
Nested Named Entity Recognition
+1
Better Transition-Based AMR Parsing with a Refined Search Space
This paper introduces a simple yet effective transition-based system for Abstract Meaning Representation (AMR) parsing.
Dependency-based Hybrid Trees for Semantic Parsing
We propose a novel dependency-based hybrid tree model for semantic parsing, which converts natural language utterance into machine interpretable meaning representations.
Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening
The spiculation quantification measures was then applied to the radiomics framework for pathological malignancy prediction with reproducible semi-automatic segmentation of nodule.
Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response
Quantification of local metabolic tumor volume (MTV) chan-ges after Chemo-radiotherapy would allow accurate tumor response evaluation.
Learning with Structured Representations for Negation Scope Extraction
We report an empirical study on the task of negation scope extraction given the negation cue.
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing
In this work, we present a study to show how learning distributed representations of the logical forms from data annotated in different languages can be used for improving the performance of a monolingual semantic parser.
SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP)
This paper describes the SemEval 2018 shared task on semantic extraction from cybersecurity reports, which is introduced for the first time as a shared task on SemEval.
Learning Latent Opinions for Aspect-Level Sentiment Classification
Aspect-level sentiment classification aims at detecting the sentiment expressed towards a particular target in a sentence.
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train.
A Unified Framework for Structured Prediction: From Theory to Practice
Based on such a framework, we show how some seemingly complicated structured prediction models such as a semantic parsing model (Lu et al., 2008; Lu, 2014) can be implemented conveniently and quickly.
Digital image splicing detection based on Markov features in QDCT and QWT domain
In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain.
Neural Architectures for Multilingual Semantic Parsing
In this paper, we address semantic parsing in a multilingual context.
Topical Coherence in LDA-based Models through Induced Segmentation
This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words.
MalwareTextDB: A Database for Annotated Malware Articles
Cybersecurity risks and malware threats are becoming increasingly dangerous and common.
Refutations on "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study"
In a recent SIGMOD paper titled "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study", Arora et al. [1] undertake a performance benchmarking study of several well-known algorithms for influence maximization.
Social and Information Networks
Rhetorical relations for information retrieval
Typically, every part in most coherent text has some plausible reason for its presence, some function that it performs to the overall semantics of the text.
Deep neural networks for learning graph representations
Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the sampling-based method for generating linear sequences proposed by Perozzi et al. (2014).
