Search Results for author:
Found 38 papers, 10 papers with code
Leveraging Three Types of Embeddings from Masked Language Models in Idiom Token Classification
Recent research has shown that contextualized word embeddings derived from masked language models (MLMs) can give promising results for idiom token classification.
Cross-lingual Linking of Automatically Constructed Frames and FrameNet
A semantic frame is a conceptual structure describing an event, relation, or object along with its participants.
On Representational Dissociation of Language and Arithmetic in Large Language Models
The association between language and (non-linguistic) thinking ability in humans has long been debated, and recently, neuroscientific evidence of brain activity patterns has been considered.
CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text
Prediction of the future citation counts of papers is increasingly important to find interesting papers among an ever-growing number of papers.
Ruri: Japanese General Text Embeddings
In this report, we provide a detailed account of the development process of Ruri.
Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs
In this study, we propose a method that replaces words with high Age of Acquisitions (AoA) in translations with simpler words to match the translations to the user's level.
Are Social Sentiments Inherent in LLMs? An Empirical Study on Extraction of Inter-demographic Sentiments
Large language models (LLMs) are supposed to acquire unconscious human knowledge and feelings, such as social common sense and biases, by training models from large amounts of text.
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages.
WikiSplit++: Easy Data Refinement for Split and Rephrase
The task of Split and Rephrase, which splits a complex sentence into multiple simple sentences with the same meaning, improves readability and enhances the performance of downstream tasks in natural language processing (NLP).
Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification
There are several linguistic claims about situations where words are more likely to be used as metaphors.
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples
To achieve this, we explore methods of data generation suitable for sentence embedding learning in this study.
Japanese SimCSE Technical Report
We report the development of Japanese SimCSE, Japanese sentence embedding models fine-tuned with SimCSE.
Transformer-based Live Update Generation for Soccer Matches from Microblog Posts
It has been known to be difficult to generate adequate sports updates from a sequence of vast amounts of diverse live tweets, although the live sports viewing experience with tweets is gaining the popularity.
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction
The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill.
Sentence Representations via Gaussian Embedding
Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual similarity (STS) task.
Semantic Frame Induction with Deep Metric Learning
Recent studies have demonstrated the usefulness of contextualized word embeddings in unsupervised semantic frame induction.
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning
Moreover, the higher model performance on difficult examples and unseen data also demonstrates the generalization ability.
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals
There have been many successful applications of sentence embedding methods.
Transformer-based Lexically Constrained Headline Generation
We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer.
Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering
Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings.
Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction
Furthermore, we examine the extent to which the contextualized representation of a verb can estimate the number of frames that the verb can evoke.
Self-Guided Curriculum Learning for Neural Machine Translation
In the field of machine learning, the well-trained model is assumed to be able to recover the training labels, i. e. the synthetic labels predicted by the model should be as close to the ground-truth labels as possible.
DefSent: Sentence Embeddings using Definition Sentences
However, these methods are only available for limited languages due to relying heavily on the large NLI datasets.
Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts
Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers{'} information access in the biomedical domain.
Investigating Word-Class Distributions in Word Vector Spaces
This paper presents an investigation on the distribution of word vectors belonging to a certain word class in a pre-trained word vector space.
Development of a Medical Incident Report Corpus with Intention and Factuality Annotation
In this paper, we present our annotation scheme with respect to the definition of medication entities that we take into account, the method to annotate the relations between entities, and the details of the intention and factuality annotation.
Incorporating Textual Information on User Behavior for Personality Prediction
Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors.
Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition
The current state-of-the-art (SOTA) model for FG-NER relies heavily on manual efforts for building a dictionary and designing hand-crafted features.
An Empirical Study on Fine-Grained Named Entity Recognition
However, there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for non-English languages.
Distinguishing Japanese Non-standard Usages from Standard Ones
Our experimental results show that the model leveraging the context embedding outperforms other methods and provide us with findings, for example, on how to construct context embeddings and which corpus to use.
Controlling Output Length in Neural Encoder-Decoders
Neural encoder-decoder models have shown great success in many sequence generation tasks.
