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Found 72 papers, 47 papers with code
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets
Work on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data.
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
Besides, our work, for the first time, provides a benchmark to evaluate and analyze the impact of different operators and normal forms by using (a) 7 choices of the operator systems and (b) 9 forms of complex queries.
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset
Experimental results show that generalizing commonsense reasoning on unseen assertions is inherently a hard task.
Exophoric Pronoun Resolution in Dialogues with Topic Regularization
Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem.
Multi-Relational Graph based Heterogeneous Multi-Task Learning in Community Question Answering
To tackle this challenge, we develop a multi-relational graph based MTL model called Heterogeneous Multi-Task Graph Isomorphism Network (HMTGIN) which efficiently solves heterogeneous CQA tasks.
Probing Toxic Content in Large Pre-Trained Language Models
Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems.
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model
Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions.
Exploring Discourse Structures for Argument Impact Classification
Discourse relations among arguments reveal logical structures of a debate conversation.
Differentially Private Federated Knowledge Graphs Embedding
However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data.
Unit Ball Model for Embedding Hierarchical Structures in the Complex Hyperbolic Space
Specifically, we propose to learn the embeddings of hierarchically structured data in the unit ball model of the complex hyperbolic space.
Variational Weakly Supervised Sentiment Analysis with Posterior Regularization
In this paper, we propose a posterior regularization framework for the variational approach to the weakly supervised sentiment analysis to better control the posterior distribution of the label assignment.
Fair Representation Learning for Heterogeneous Information Networks
Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness.
ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities
After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them.
Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks
Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.
Wasserstein diffusion on graphs with missing attributes
Graph neural networks have been demonstrated powerful in graph representation learning, however, they rely heavily on the completeness of graph information.
Joint Coreference Resolution and Character Linking for Multiparty Conversation
Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations.
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Graph convolutional networks have achieved great success on graph-structured data.
DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge
On the other hand, generation models have the potential to automatically generate more knowledge.
CoCoLM: COmplex COmmonsense Enhanced Language Model
Large-scale pre-trained language models have demonstrated strong knowledge representation ability.
Learning Contextual Causality from Time-consecutive Images
Compared with pure text-based approaches, learning causality from the visual signal has the following advantages: (1) Causality knowledge belongs to the commonsense knowledge, which is rarely expressed in the text but rich in videos; (2) Most events in the video are naturally time-ordered, which provides a rich resource for us to mine causality knowledge from; (3) All the objects in the video can be used as context to study the contextual property of causal relations.
Analogous Process Structure Induction for Sub-event Sequence Prediction
Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories.
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models
We also demonstrate that distributional methods are ideal to make up for pattern-based ones in such cases.
A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution
Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to.
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning
To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model.
Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network
Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network and further present a new GNN model, called R^2GCN, which intrinsically works for the heterogeneous networks, to achieve generalizable student performance prediction in interactive online question pools.
Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations
Computational and cognitive studies suggest that the abstraction of eventualities (activities, states, and events) is crucial for humans to understand daily eventualities.
WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge
Experimental results prove that even though pre-trained language representation models have achieved promising progress on the original WSC dataset, they are still struggling at WinoWhy.
SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems
Experiments on three data sets verify the effectiveness of our model and show that it outperforms state-of-the-art social recommendation methods.
TransOMCS: From Linguistic Graphs to Commonsense Knowledge
Commonsense knowledge acquisition is a key problem for artificial intelligence.
On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at both the text span level and the sentence level.
A Chinese Corpus for Fine-grained Entity Typing
In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4, 800 mentions manually labeled through crowdsourcing.
Fisher Deep Domain Adaptation
Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance.
On the Role of Conceptualization in Commonsense Knowledge Graph Construction
Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to handle highly diverse queries in natural language related to commonsense, leads to unique challenges for automatic KG construction methods.
Vertex-reinforced Random Walk for Network Embedding
In this paper, we study the fundamental problem of random walk for network embedding.
Multiplex Word Embeddings for Selectional Preference Acquisition
Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words.
Neural Subgraph Isomorphism Counting
In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.
RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved
However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved.
Improving Fine-grained Entity Typing with Entity Linking
Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention.
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
Heterogeneous information network (HIN) embedding has gained increasing interests recently.
What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues
To tackle this challenge, in this paper, we formally define the task of visual-aware pronoun coreference resolution (PCR) and introduce VisPro, a large-scale dialogue PCR dataset, to investigate whether and how the visual information can help resolve pronouns in dialogues.
Multilingual and Multi-Aspect Hate Speech Analysis
Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks.
Motif Enhanced Recommendation over Heterogeneous Information Network
In this paper, we propose to use motifs to capture higher-order relations among nodes of same type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine motif-based higher-order relations with edge-based first-order relations.
Hyper-Path-Based Representation Learning for Hyper-Networks
Then a carefully designed algorithm, Hyper-gram, utilizes these random walks to capture both pairwise relationships and tuplewise relationships in the whole hyper-networks.
Knowledge-aware Pronoun Coreference Resolution
Resolving pronoun coreference requires knowledge support, especially for particular domains (e. g., medicine).
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
Lack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews.
Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pair-wise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model.
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content.
Incorporating Context and External Knowledge for Pronoun Coreference Resolution
Linking pronominal expressions to the correct references requires, in many cases, better analysis of the contextual information and external knowledge.
SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition
Selectional Preference (SP) is a commonly observed language phenomenon and proved to be useful in many natural language processing tasks.
ASER: A Large-scale Eventuality Knowledge Graph
Understanding human's language requires complex world knowledge.
Relation Discovery with Out-of-Relation Knowledge Base as Supervision
In this paper, we study the problem of how to use out-of-relation knowledge bases to supervise the discovery of unseen relations, where out-of-relation means that relations to discover from the text corpus and those in knowledge bases are not overlapped.
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification
These word pairs can be extracted by using dependency parsers and simple rules.
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.
Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification
Hierarchical text classification has many real-world applications.
MetaGAN: An Adversarial Approach to Few-Shot Learning
In this paper, we propose a conceptually simple and general framework called MetaGAN for few-shot learning problems.
Entity Linking within a Social Media Platform: A Case Study on Yelp
In this paper, we study a new entity linking problem where both the entity mentions and the target entities are within a same social media platform.
Side Information Fusion for Recommender Systems over Heterogeneous Information Network
Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users' preferences (ratings) based on their past behaviors.
Understanding Hidden Memories of Recurrent Neural Networks
We propose a technique to explain the function of individual hidden state units based on their expected response to input texts.
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension
Document-level multi-aspect sentiment classification is an important task for customer relation management.
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components
Word embeddings have attracted much attention recently.
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings
In this paper, we propose a simple and effective ensemble method to further boost the performances of neural models.
Subjective Knowledge Acquisition and Enrichment Powered By Crowdsourcing
In our work, we propose a system, called crowdsourced subjective knowledge acquisition (CoSKA), for subjective knowledge acquisition powered by crowdsourcing and existing KBs.
Machine Learning with World Knowledge: The Position and Survey
In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work.
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted.
Cross-lingual Dataless Classification for Languages with Small Wikipedia Presence
We use a word-level dictionary to convert documents in a SWL to a large-Wikipedia language (LWLs), and then perform CLDDC based on the LWL's Wikipedia.
World Knowledge as Indirect Supervision for Document Clustering
We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network.
Tracking Idea Flows between Social Groups
To facilitate users in analyzing the flow, we present a method to model the flow behaviors that aims at identifying the lead-lag relationships between word clusters of different user groups.