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Found 140 papers, 70 papers with code
Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts
In this paper, we argue that different social media posts should consider different modalities for multimodal information extraction.
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning
In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process.
PowerAttention: Exponentially Scaling of Receptive Fields for Effective Sparse Attention
Large Language Models (LLMs) face efficiency bottlenecks due to the quadratic complexity of the attention mechanism when processing long contexts.
MciteBench: A Benchmark for Multimodal Citation Text Generation in MLLMs
To address this gap, we introduce MCiteBench, the first benchmark designed to evaluate and analyze the multimodal citation text generation ability of MLLMs.
Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation
To address this issue, we introduce an order-centric data augmentation framework based on commutativity in logical reasoning.
Reward Shaping to Mitigate Reward Hacking in RLHF
Reinforcement Learning from Human Feedback (RLHF) is essential for aligning large language models (LLMs) with human values.
Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following
Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs).
DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling
To advance personalized applications such as recommendation systems and user behavior prediction, recent research increasingly adopts large language models (LLMs) for human -readable persona modeling.
CoSER: Coordinating LLM-Based Persona Simulation of Established Roles
It provides authentic dialogues with real-world intricacies, as well as diverse data types such as conversation setups, character experiences and internal thoughts.
AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model
Specifically, to efficiently query the LLM, we propose an adaptive selection strategy based on the uncertainty estimation of the SLM, where the LLM is invoked only when the SLM is uncertain.
CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory
Large Language Models (LLMs) have demonstrated outstanding capabilities across various domains, but the increasing complexity of new challenges demands enhanced performance and adaptability.
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models
It is crucial for large language models (LLMs) to follow instructions that involve multiple constraints.
MultiLingPoT: Enhancing Mathematical Reasoning with Multilingual Program Fine-tuning
Since different programming languages excel in different areas, it is natural to use the most suitable language for solving specific problems.
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs
However, most PPR-based GNNs are designed for static graphs, and efficient PPR maintenance remains as an open problem.
QUILL: Quotation Generation Enhancement of Large Language Models
To improve the LLMs' quotation generation abilities, we construct a bilingual knowledge base that is broad in scope and rich in dimensions, containing up to 32, 022 quotes.
Faster Local Solvers for Graph Diffusion Equations
Standard iterative methods require accessing the whole graph per iteration, making them time-consuming for large-scale graphs.
Iterative Methods via Locally Evolving Set Process
We propose to use the \textit{locally evolving set process}, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized.
Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models
To address these limitations, we propose Progressive Thought Refinement (PTR), a framework that enables LLMs to refine their responses progressively.
Revealing the Barriers of Language Agents in Planning
Although existing studies have highlighted weak performance in agent planning, the deeper underlying issues and the mechanisms and limitations of the strategies proposed to address them remain insufficiently understood.
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective
To address these deficiencies, we propose a novel metric called SemVarEffect and a benchmark named SemVarBench, designed to evaluate the causality between semantic variations in inputs and outputs in T2I synthesis.
SEGMENT+: Long Text Processing with Short-Context Language Models
There is a growing interest in expanding the input capacity of language models (LMs) across various domains.
Do Large Language Models have Problem-Solving Capability under Incomplete Information Scenarios?
Therefore, in this paper, we introduce a novel game named BrainKing based on the ``Who is undercover'' and ``Twenty Questions'' for evaluating LLM capabilities under incomplete information scenarios.
Past Meets Present: Creating Historical Analogy with Large Language Models
Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world.
HOTVCOM: Generating Buzzworthy Comments for Videos
In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose.
Recent Advancement of Emotion Cognition in Large Language Models
Emotion cognition in large language models (LLMs) is crucial for enhancing performance across various applications, such as social media, human-computer interaction, and mental health assessment.
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
The framework includes four distinctive tasks: Key Event Recognition, Mixed Event Recognition, Implicit Emotional Recognition, and Intention Recognition.
TravelAgent: An AI Assistant for Personalized Travel Planning
As global tourism expands and artificial intelligence technology advances, intelligent travel planning services have emerged as a significant research focus.
LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models
To address the lack of expert knowledge and enhance log understanding for smaller PLMs, this paper introduces a novel and practical knowledge enhancement framework, called LUK, which acquires expert knowledge from LLMs automatically and then enhances the smaller PLM for log analysis with these expert knowledge.
LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection
Explainable fake news detection predicts the authenticity of news items with annotated explanations.
Dr.Academy: A Benchmark for Evaluating Questioning Capability in Education for Large Language Models
Therefore, our research introduces a benchmark to evaluate the questioning capability in education as a teacher of LLMs through evaluating their generated educational questions, utilizing Anderson and Krathwohl's taxonomy across general, monodisciplinary, and interdisciplinary domains.
XMeCap: Meme Caption Generation with Sub-Image Adaptability
Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines.
Can Pre-trained Language Models Understand Chinese Humor?
Humor understanding is an important and challenging research in natural language processing.
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization
Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community.
Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems.
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation
Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks.
Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs
For dataset construction, we create KnowReason via rule mining on KGs.
Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data
Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia. While existing RPAs well portray the characters' knowledge and tones, they face challenges in capturing their minds, especially for small role-playing language models (RPLMs).
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models
Inspired by the awesome development of role-playing agents, we propose an ESC Evaluation framework (ESC-Eval), which uses a role-playing agent to interact with ESC models, followed by a manual evaluation of the interactive dialogues.
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
This paper proposes a benchmark called DetectBench for verifying the ability to detect and piece together implicit evidence within a long context.
Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals
Large language models (LLMs) have achieved great success, but their occasional content fabrication, or hallucination, limits their practical application.
Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models
Specifically, we introduce COG, a two-stage framework with COncept-Guided vision-language models.
VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It
Online courses have significantly lowered the barrier to accessing education, yet the varying content quality of these videos poses challenges.
StrucText-Eval: Evaluating Large Language Model's Reasoning Ability in Structure-Rich Text
The effective utilization of structured data, integral to corporate data strategies, has been challenged by the rise of large language models (LLMs) capable of processing unstructured information.
SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals
Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming.
SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation
Existing Large Language Models (LLMs) generate text through unidirectional autoregressive decoding methods to respond to various user queries.
From Persona to Personalization: A Survey on Role-Playing Language Agents
Through this work, we aim to establish a clear taxonomy of RPLA research and applications, and facilitate future research in this critical and ever-evolving field, and pave the way for a future where humans and RPLAs coexist in harmony.
From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models
It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i. e. Complex Instructions Following).
AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation
In this work, we introduce the paradigm of generating web scrapers with LLMs and propose AutoScraper, a two-stage framework that can handle diverse and changing web environments more efficiently.
Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?
Can Large Language Models (LLMs) simulate humans in making important decisions?
Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience
Specifically, we first identify three key problems: (1) How to capture the inherent confidence of the LLM?
Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency
To achieve NTE, we devise a novel Syntax&Semantic-Enhanced Negation Extraction model, namely SSENE, which is built based on a generative pretrained language model (PLM) {of Encoder-Decoder architecture} with a multi-task learning framework.
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction
The framework includes an evaluation model that can extract related entity pairs with high precision.
CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
As world knowledge advances and new task schemas emerge, Continual Learning (CL) becomes essential for keeping Large Language Models (LLMs) current and addressing their shortcomings.
SurveyAgent: A Conversational System for Personalized and Efficient Research Survey
In the rapidly advancing research fields such as AI, managing and staying abreast of the latest scientific literature has become a significant challenge for researchers.
How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?
By leveraging the retrieval of information from external knowledge databases, Large Language Models (LLMs) exhibit enhanced capabilities for accomplishing many knowledge-intensive tasks.
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
Definition bias is a negative phenomenon that can mislead models.
AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior
Language significantly influences the formation and evolution of Human emergent behavior, which is crucial in understanding collective intelligence within human societies.
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks
In our experiments, we find that atomic skills can not spontaneously generalize to compositional tasks.
Efficiently Quantifying and Mitigating Ripple Effects in Model Editing
Large Language Models have revolutionized numerous tasks with their remarkable efficacy.
OVEL: Large Language Model as Memory Manager for Online Video Entity Linking
In this paper, we propose a task called Online Video Entity Linking OVEL, aiming to establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness.
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick
Large language models (LLMs) excellently generate human-like text, but also raise concerns about misuse in fake news and academic dishonesty.
TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation
Despite remarkable advancements in emulating human-like behavior through Large Language Models (LLMs), current textual simulations do not adequately address the notion of time.
TravelPlanner: A Benchmark for Real-World Planning with Language Agents
Are these language agents capable of planning in more complex settings that are out of the reach of prior AI agents?
Exploiting Duality in Open Information Extraction with Predicate Prompt
Open information extraction (OpenIE) aims to extract the schema-free triplets in the form of (\emph{subject}, \emph{predicate}, \emph{object}) from a given sentence.
Small Language Model Can Self-correct
In this paper, we introduce the \underline{I}ntrinsic \underline{S}elf-\underline{C}orrection (ISC) in generative language models, aiming to correct the initial output of LMs in a self-triggered manner, even for those small LMs with 6 billion parameters.
ConcEPT: Concept-Enhanced Pre-Training for Language Models
In this paper, we propose ConcEPT, which stands for Concept-Enhanced Pre-Training for language models, to infuse conceptual knowledge into PLMs.
Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception
Hence, we present a framework to enhance the quantitative reasoning ability of language models based on dimension perception.
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge Base
Comprising 951K images and 152K concepts, M^2ConceptBase links each concept to an average of 6. 27 images and a single description, ensuring comprehensive visual and textual semantics.
A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity Metrics
We use Winoground-T2I with a dual objective: to evaluate the performance of T2I models and the metrics used for their evaluation.
Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sources
Pre-trained language models (PLMs) have established the new paradigm in the field of NLP.
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
Then, with InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80. 7%.
Can Large Language Models Understand Real-World Complex Instructions?
To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
Towards Visual Taxonomy Expansion
Specifically, on the Chinese taxonomy dataset, our method significantly improves accuracy by 8. 75 %.
Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models
To translate well, machine translation (MT) systems and general-purposed language models (LMs) need a deep understanding of both source and target languages and cultures.
KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability.
AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities
Multi-modal knowledge graphs (MMKGs) combine different modal data (e. g., text and image) for a comprehensive understanding of entities.
Piecing Together Clues: A Benchmark for Evaluating the Detective Skills of Large Language Models
We introduces the DetectBench, a reading comprehension dataset designed to assess a model's ability to jointly ability in key information detection and multi-hop reasoning when facing complex and implicit information.
Adaptive Ordered Information Extraction with Deep Reinforcement Learning
Information extraction (IE) has been studied extensively.
M3PT: A Multi-Modal Model for POI Tagging
In this paper, we propose a novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI's textual and visual features, and the precise matching between the multi-modal representations.
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation
Similes play an imperative role in creative writing such as story and dialogue generation.
QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search
In this paper, we propose QUERT, A Continual Pre-trained Language Model for QUERy Understanding in Travel Domain Search.
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs).
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction
The vital role of analogical reasoning in human cognition allows us to grasp novel concepts by linking them with familiar ones through shared relational structures.
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge
Large language models (LLMs) have been widely studied for their ability to store and utilize positive knowledge.
ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base
Analogical reasoning is a fundamental cognitive ability of humans.
Distilling Script Knowledge from Large Language Models for Constrained Language Planning
In everyday life, humans often plan their actions by following step-by-step instructions in the form of goal-oriented scripts.
Causality-aware Concept Extraction based on Knowledge-guided Prompting
The prompt adopts the topic of the given entity from the existing knowledge in KGs to mitigate the spurious co-occurrence correlations between entities and biased concepts.
Domain Mastery Benchmark: An Ever-Updating Benchmark for Evaluating Holistic Domain Knowledge of Large Language Model--A Preliminary Release
Domain knowledge refers to the in-depth understanding, expertise, and familiarity with a specific subject, industry, field, or area of special interest.
Sem4SAP: Synonymous Expression Mining From Open Knowledge Graph For Language Model Synonym-Aware Pretraining
The model's ability to understand synonymous expression is crucial in many kinds of downstream tasks.
GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation
The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts.
BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark
Our aim is to facilitate research in the development of NLP within the Chinese financial domain.
MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base
The ability to understand and generate similes is an imperative step to realize human-level AI.
Harnessing Knowledge and Reasoning for Human-Like Natural Language Generation: A Brief Review
The rapid development and application of natural language generation (NLG) techniques has revolutionized the field of automatic text production.
Group Buying Recommendation Model Based on Multi-task Learning
Furthermore, we propose two auxiliary losses corresponding to the two sub-tasks, to refine the representation learning in our model.
Converge to the Truth: Factual Error Correction via Iterative Constrained Editing
Given a possibly false claim sentence, how can we automatically correct it with minimal editing?
Improving Continual Relation Extraction through Prototypical Contrastive Learning
Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks.
Generative Entity Typing with Curriculum Learning
To overcome these drawbacks, we propose a novel generative entity typing (GET) paradigm: given a text with an entity mention, the multiple types for the role that the entity plays in the text are generated with a pre-trained language model (PLM).
Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension
In order to supply existing KGs with more fine-grained and new concepts, we propose a novel concept extraction framework, namely MRC-CE, to extract large-scale multi-granular concepts from the descriptive texts of entities.
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential Recommendation
Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a flat graph without hierarchy, overlooking diverse factors in the user's preference.
Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation
The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance.
Language Models as Knowledge Embeddings
In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods.
Ranked #5 on
Link Prediction
on WN18RR
Tackling Math Word Problems with Fine-to-Coarse Abstracting and Reasoning
Math Word Problems (MWP) is an important task that requires the ability of understanding and reasoning over mathematical text.
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints
To this end, we propose a plug-in algorithm for this line of work, i. e., Aligned Constrained Training (ACT), which alleviates this problem by familiarizing the model with the source-side context of the constraints.
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types
In this paper, we present WikiDiverse, a high-quality human-annotated MEL dataset with diversified contextual topics and entity types from Wikinews, which uses Wikipedia as the corresponding knowledge base.
Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision
Specifically, i) to fully exploit user behavioral information, we extract candidate hyponymy relations that match user interests from query-click concepts; ii) to enhance the semantic information of new concepts and better detect hyponymy relations, we model concepts and relations through both user-generated content and structural information in existing taxonomies and user click logs, by leveraging Pre-trained Language Models and Graph Neural Network combined with Contrastive Learning; iii) to reduce the cost of dataset construction and overcome data skews, we construct a high-quality and balanced training dataset from existing taxonomy with no supervision.
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning
Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR).
Can Pre-trained Language Models Interpret Similes as Smart as Human?
In this paper, we investigate the ability of PLMs in simile interpretation by designing a novel task named Simile Property Probing, i. e., to let the PLMs infer the shared properties of similes.
Rule Mining over Knowledge Graphs via Reinforcement Learning
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.
Multi-Modal Knowledge Graph Construction and Application: A Survey
In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques.
Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation
In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models.
Semantic-based Data Augmentation for Math Word Problems
In this paper, we propose a set of novel data augmentation approaches to supplement existing datasets with such data that are augmented with different kinds of local variances, and help to improve the generalization ability of current neural models.
Unsupervised Editing for Counterfactual Stories
We also propose a new metric to alleviate the shortcomings of current automatic metrics and better evaluate the trade-off.
A Review on Graph Neural Network Methods in Financial Applications
With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations.
A Probit Tensor Factorization Model For Relational Learning
A large number of models and algorithms have been proposed to perform link prediction, among which tensor factorization method has proven to achieve state-of-the-art performance in terms of computation efficiency and prediction accuracy.
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion
Today data is often scattered among billions of resource-constrained edge devices with security and privacy constraints.
Relation Aware Semi-autoregressive Semantic Parsing for NL2SQL
It first learns relation embedding over the schema entities and question words with predefined schema relations with ELECTRA and relation aware transformer layer as backbone.
Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction
As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the model continuously.
Revisiting the Negative Data of Distantly Supervised Relation Extraction
Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem.
Ranked #1 on
Relation Extraction
on NYT11-HRL
A Question-answering Based Framework for Relation Extraction Validation
However, few works have focused on how to validate and correct the results generated by the existing relation extraction models.
LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification
The final claim verification is based on all latent variables.
InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction
We see information sources as multiple views and fusing them to construct an intact space with sufficient information.
Complex Relation Extraction: Challenges and Opportunities
In recent years, many complex relation extraction tasks, i. e., the variants of simple binary relation extraction, are proposed to meet the complex applications in practice.
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
Effectively filtering out noisy articles as well as bad answers is the key to improving extraction accuracy.
Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty
Most of existing embedding based recommendation models use embeddings (vectors) corresponding to a single fixed point in low-dimensional space, to represent users and items.
A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention
Deep neural networks (DNNs) have been widely employed in recommender systems including incorporating attention mechanism for performance improvement.
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation
These insights motivate us to propose a novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors and item Knowledge into Multi-task learning for Session-based Recommendation.
Attacking Recommender Systems with Augmented User Profiles
Recommendation Systems (RS) have become an essential part of many online services.
Collective Loss Function for Positive and Unlabeled Learning
On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions.
Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment
We calibrate embeddings of different KGs via a small set of pre-aligned seeds.
Interpretable Text Classification Using CNN and Max-pooling
Deep neural networks have been widely used in text classification.
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks
Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation.
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity.
KBQA: Learning Question Answering over QA Corpora and Knowledge Bases
Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions.
CN-Probase: A Data-driven Approach for Large-scale Chinese Taxonomy Construction
Taxonomies play an important role in machine intelligence.
Deep Short Text Classification with Knowledge Powered Attention
For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA).
Verb Pattern: A Probabilistic Semantic Representation on Verbs
In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb.
Entity Suggestion by Example using a Conceptual Taxonomy
This paper provides a query processing method based on the relevance models between entity sets and concepts.
