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Found 112 papers, 30 papers with code
CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers
To bridge the gap, we propose a new task: conditional question answering with hierarchical multi-span answers, where both the hierarchical relations and the conditions need to be extracted.
Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation
In this work, we propose a knowledge transfer method with visual prompt (VPTG) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle visual dialogue tasks.
CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts
This paper presents a description of our system in SMM4H-2022, where we participated in task 1a, task 4, and task 6 to task 10.
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article.
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts
In fact, the semantics of a relation can be expressed by three kinds of graphs: factual graph, ontology graph, textual description graph, and they can complement each other.
Generating Temporally-ordered Event Sequences via Event Optimal Transport
The other issue is that the model adopts a word-level objective to model events in texts, failing to evaluate the predicted results of the model from the perspective of event sequence.
Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach
Currently existing approach models this task as a statistical induction problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events.
CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities
This paper describes our approach to develop a complex named entity recognition system in SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition, Track 9 - Chinese.
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer
For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages.
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing
Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases.
Incremental Intent Detection for Medical Domain with Contrast Replay Networks
We first formulate incremental learning for medical intent detection.
Chinese Named Entity Recognition via Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism
Specifically, to reduce the errors of predicting entity boundaries, we propose an adaptive multi-pass memory network to exploit lexical knowledge.
Chinese Named Entity Recognition
named-entity-recognition
+3
CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset
2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches.
Set Generation Networks for End-to-End Knowledge Base Population
In this paper, we formulate end-to-end KBP as a direct set generation problem, avoiding considering the order of multiple facts.
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations
Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format.
Biomedical Concept Normalization by Leveraging Hypernyms
Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module.
Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification
Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information.
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks
This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains.
Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks
This is the system description of the CASIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021.
Scene Restoring for Narrative Machine Reading Comprehension
Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension.
Event Extraction as Machine Reading Comprehension
ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49. 8{\%} in F1 for event argument extraction with only 1{\%} data, compared with 2. 2{\%} of the previous method.
Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs.
Large Language Models are reasoners with Self-Verification
We propose a new method called self-verification that uses the conclusion of the CoT as a condition to build a new sample and asks the LLM to re-predict the original conditions which be masked.
Generating Hierarchical Explanations on Text Classification Without Connecting Rules
The opaqueness of deep NLP models has motivated the development of methods for interpreting how deep models predict.
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively.
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains
Recently, natural language database (NLDB) conducts complex QA in knowledge base with textual evidences rather than structured representations, this task attracts a lot of attention because of the flexibility and richness of textual evidence.
MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation
This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences.
Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation
In this work, we propose to model multi-grained popularity features and jointly learn them together with high-order connectivity, to match the differentiation of user preferences exhibited in popularity features.
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder
In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning problem is one of the most typical and challenging problems.
MALICE: Manipulation Attacks on Learned Image ComprEssion
To characterize the robustness of state-of-the-art learned image compression, we mount white-box and black-box attacks.
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic.
Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area
Infectious diseases usually originate from a specific location within a city.
Logic Traps in Evaluating Attribution Scores
Meanwhile, since the reasoning process of deep models is inaccessible, researchers design various evaluation methods to demonstrate their arguments.
Lifelong Intent Detection via Multi-Strategy Rebalancing
In this paper, we propose Lifelong Intent Detection (LID), which continually trains an ID model on new data to learn newly emerging intents while avoiding catastrophically forgetting old data.
Alignment Rationale for Natural Language Inference
Deep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors.
Document-level Event Extraction via Parallel Prediction Networks
We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document.
Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism
The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes.
Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks
Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge.
A Large-Scale Chinese Multimodal NER Dataset with Speech Clues
In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents.
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement
Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training.
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification
On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework and can interactively adjust the generation process to generate task-related sentences.
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion
To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC.
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents
This paper proposes a new task regarding event reason extraction from document-level texts.
Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction
Furthermore, to better fit with convolutions, we suggest to first aggregate traffic flows according to pre-conceived regions or self-organized regions based on traffic flows, then dispose to sequentially organized raster images for network input.
Graph-Based Knowledge Integration for Question Answering over Dialogue
Despite many advances, existing approaches for this task did not consider dialogue structure and background knowledge (e. g., relationships between speakers).
Ranked #6 on
Question Answering
on FriendsQA
Reconstructing Event Regions for Event Extraction via Graph Attention Networks
Event information is usually scattered across multiple sentences within a document.
Joint Entity and Relation Extraction with Set Prediction Networks
Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities.
Ranked #1 on
Joint Entity and Relation Extraction
on NYT
How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization
Event detection (ED) aims to identify and classify event triggers in texts, which is a crucial subtask of event extraction (EE).
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision
Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora.
Pre-trained Language Model Based Active Learning for Sentence Matching
Active learning is able to significantly reduce the annotation cost for data-driven techniques.
Towards Causal Explanation Detection with Pyramid Salient-Aware Network
PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network.
Event Coreference Resolution via a Multi-loss Neural Network without Using Argument Information
Event coreference resolution(ECR) is an important task in Natural Language Processing (NLP) and nearly all the existing approaches to this task rely on event argument information.
Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images
Unprecedented data collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools that remove sensitive attributes from images while maintaining useful information for other tasks.
Distantly Supervised Relation Extraction in Federated Settings
In this paper, we propose a federated denoising framework to suppress label noise in federated settings.
Connecting Embeddings for Knowledge Graph Entity Typing
In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge from KGs.
RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding
In this work, we develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF), where the construction of the embeddings of users (items) are delicately redesigned from several aspects during the aggregation on the graph.
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding
Specifically, we propose a hyperbolic representation method to leverage the code hierarchy.
Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes
In this paper, we introduce Clinical-Coder, an online system aiming to assign ICD codes to Chinese clinical notes.
MIE: A Medical Information Extractor towards Medical Dialogues
Electronic Medical Records (EMRs) have become key components of modern medical care systems.
Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks
Deep learning (DL) offers potential improvements throughout the CAD tool-flow, one promising application being lithographic hotspot detection.
Knowledge Guided Metric Learning for Few-Shot Text Classification
This is mainly due to the fact that human beings can leverage knowledge obtained from relevant tasks.
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
NNoculation: Catching BadNets in the Wild
This paper proposes a novel two-stage defense (NNoculation) against backdoored neural networks (BadNets) that, repairs a BadNet both pre-deployment and online in response to backdoored test inputs encountered in the field.
Neural Cross-Lingual Event Detection with Minimal Parallel Resources
In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources.
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system.
Ranked #10 on
Chinese Named Entity Recognition
on Weibo NER
Chinese Named Entity Recognition
named-entity-recognition
+2
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
Existing works didn{'}t consider the extraction order of relational facts in a sentence.
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network
Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph.
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots
Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC.
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss
Meanwhile, such generated question can express the given predicate and correspond to a definitive answer.
Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking
How to integrate the semantic information of pre-defined ontology and dialogue text (heterogeneous texts) to generate unknown values and improve performance becomes a severe challenge.
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation
To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation.
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing
To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens.
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection
There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning.
Patch alignment manifold matting
In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting.
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms
Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information.
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS.
Ranked #1 on
Chinese Named Entity Recognition
on SighanNER
Chinese Named Entity Recognition
Chinese Word Segmentation
+4
Event Detection via Gated Multilingual Attention Mechanism
In specific, to alleviate data scarcity problem, we exploit the consistent information in multilingual data via context attention mechanism.
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases
Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks.
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
The relational facts in sentences are often complicated.
Ranked #13 on
Relation Extraction
on WebNLG
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data
We present an event extraction framework to detect event mentions and extract events from the document-level financial news.
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
Our work provides the first step toward defenses against backdoor attacks in deep neural networks.
IJCNLP-2017 Task 5: Multi-choice Question Answering in Examinations
The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1.
Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms
This paper tackles the task of event detection (ED), which involves identifying and categorizing events.
Automatically Labeled Data Generation for Large Scale Event Extraction
Modern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data.
Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors
Solving cold-start problem in review spam detection is an urgent and significant task.
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge
This simple representation strategy is not easy to express the proper information in the question.
Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning
Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response.
Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?
As one of the most important test of China, Gaokao is designed to be difficult enough to distinguish the excellent high school students.
Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information
With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important.
How to Generate a Good Word Embedding?
We analyze three critical components of word embedding training: the model, the corpus, and the training parameters.
Knowledge Graph Embedding via Dynamic Mapping Matrix
Ranked #8 on
Link Prediction
on FB15k
(MR metric)
Recurrent Convolutional Neural Networks for Text Classification
The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.
Ranked #4 on
Emotion Recognition in Conversation
on CPED
