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Found 41 papers, 9 papers with code
Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes
We propose a methodology for representing the reasoning structure of arguments using Bayesian networks and predicate logic facilitated by argumentation schemes.
A Simple but Effective Closed-form Solution for Extreme Multi-label Learning
Many current high-performance XML models are composed of a lot of hyperparameters, which complicates the tuning process.
Can Impressions of Music be Extracted from Thumbnail Images?
In recent years, there has been a notable increase in research on machine learning models for music retrieval and generation systems that are capable of taking natural language sentences as inputs.
Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain
Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries.
How Panel Layouts Define Manga: Insights from Visual Ablation Experiments
Today, manga has gained worldwide popularity.
Theoretical Aspects of Bias and Diversity in Minimum Bayes Risk Decoding
We also introduce a new MBR approach, Metric-augmented MBR (MAMBR), which increases diversity by adjusting the behavior of utility functions without altering the pseudo-references.
Towards Cross-Lingual Explanation of Artwork in Large-scale Vision Language Models
As the performance of Large-scale Vision Language Models (LVLMs) improves, they are increasingly capable of responding in multiple languages, and there is an expectation that the demand for explanations generated by LVLMs will grow.
Multi-label Learning with Random Circular Vectors
The extreme multi-label classification~(XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
+1
Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding
This paper provides theoretical interpretations of the smoothing methods for the NS loss in KGE and induces a new NS loss, Triplet Adaptive Negative Sampling (TANS), that can cover the characteristics of the conventional smoothing methods.
Artwork Explanation in Large-scale Vision Language Models
To address this issue, we propose a new task: the artwork explanation generation task, along with its evaluation dataset and metric for quantitatively assessing the understanding and utilization of knowledge about artworks.
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models
We validate it using a dataset of images from 15 categories, each with five critic review texts and annotated rankings in both English and Japanese, totaling over 2, 000 data instances.
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?
Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities in a KG.
Model-based Subsampling for Knowledge Graph Completion
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing overfitting caused by the sparsity in Knowledge Graph (KG) datasets.
Implicit ZCA Whitening Effects of Linear Autoencoders for Recommendation
Recently, in the field of recommendation systems, linear regression (autoencoder) models have been investigated as a way to learn item similarity.
Using Wikipedia Editor Information to Build High-performance Recommender Systems
Wikipedia has high-quality articles on a variety of topics and has been used in diverse research areas.
Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models
This task consists of two parts: the first is to generate a table containing knowledge about an entity and its related image, and the second is to generate an image from an entity with a caption and a table containing related knowledge of the entity.
Subsampling for Knowledge Graph Embedding Explained
In this article, we explain the recent advance of subsampling methods in knowledge graph embedding (KGE) starting from the original one used in word2vec.
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
To solve this problem, we theoretically analyzed NS loss to assist hyperparameter tuning and understand the better use of the NS loss in KGE learning.
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling
On the other hand, properties of the NS loss function that are considered important for learning, such as the relationship between the noise distribution and the number of negative samples, have not been investigated theoretically.
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding
In knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated.
Ranked #15 on
Link Prediction
on FB15k-237
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP.
A System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
Analyzing Word Embedding Through Structural Equation Modeling
Many researchers have tried to predict the accuracies of extrinsic evaluation by using intrinsic evaluation to evaluate word embedding.
Binarized Canonical Polyadic Decomposition for Knowledge Graph Completion
Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion. However, embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vectors are large.
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs
Although they perform well in predicting atomic relations, composite relations (relation paths) cannot be modeled naturally by the product of relation matrices, as the product of diagonal matrices is commutative and hence invariant with the order of relations.
Binarized Knowledge Graph Embeddings
This limitation is expected to become more stringent as existing knowledge graphs, which are already huge, keep steadily growing in scale.
Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric and Circulant Weight Matrices
Currently, the biaffine classifier has been attracting attention as a method to introduce an attention mechanism into the modeling of binary relations.
Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion
Embedding-based methods for knowledge base completion (KBC) learn representations of entities and relations in a vector space, along with the scoring function to estimate the likelihood of relations between entities.
Higher-Order Syntactic Attention Network for Longer Sentence Compression
To solve this problem, we propose a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states.
Ranked #3 on
Sentence Compression
on Google Dataset
Neural Tensor Networks with Diagonal Slice Matrices
Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time.
Supervised Attention for Sequence-to-Sequence Constituency Parsing
The sequence-to-sequence (Seq2Seq) model has been successfully applied to machine translation (MT).
Hierarchical Word Structure-based Parsing: A Feasibility Study on UD-style Dependency Parsing in Japanese
We introduce the following hierarchical word structures to dependency parsing in Japanese: morphological units (a short unit word, SUW) and syntactic units (a long unit word, LUW).
K-best Iterative Viterbi Parsing
This paper presents an efficient and optimal parsing algorithm for probabilistic context-free grammars (PCFGs).
On the Equivalence of Holographic and Complex Embeddings for Link Prediction
We show the equivalence of two state-of-the-art link prediction/knowledge graph completion methods: Nickel et al's holographic embedding and Trouillon et al.'s complex embedding.
Empirical comparison of dependency conversions for RST discourse trees
Ranked #5 on
Discourse Parsing
on RST-DT
(RST-Parseval (Full) metric)
Efficient Stacked Dependency Parsing by Forest Reranking
This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing.
