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Found 65 papers, 9 papers with code
JGLUE: Japanese General Language Understanding Evaluation
We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese.
A Method for Building a Commonsense Inference Dataset based on Basic Events
We present a scalable, low-bias, and low-cost method for building a commonsense inference dataset that combines automatic extraction from a corpus and crowdsourcing.
Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance
We investigate the impact of politeness levels in prompts on the performance of large language models (LLMs).
SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition
Audio-visual speech recognition (AVSR) is a multimodal extension of automatic speech recognition (ASR), using video as a complement to audio.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Exploring Automatic Evaluation Methods based on a Decoder-based LLM for Text Generation
Automatic evaluation of text generation is essential for improving the accuracy of generation tasks.
PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a Language Model
In this paper, we propose PHALM, a method of building a knowledge graph from scratch, by prompting both crowdworkers and a large language model (LLM).
Kanbun-LM: Reading and Translating Classical Chinese in Japanese Methods by Language Models
To solve this problem, we construct the first Classical-Chinese-to-Kanbun dataset in the world.
Grounding in social media: An approach to building a chit-chat dialogue model
Building open-domain dialogue systems capable of rich human-like conversational ability is one of the fundamental challenges in language generation.
Building a Personalized Dialogue System with Prompt-Tuning
Dialogue systems without consistent responses are not fascinating.
Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation
We aim to overcome the lack of diversity in responses of current dialogue systems and to develop a dialogue system that is engaging as a conversational partner.
Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions
We hope that the constructed corpus will facilitate the study on emotion recognition in a dialogue and emotion-aware dialogue response generation.
Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation
For a computer to naturally interact with a human, it needs to be human-like.
BERT-based Cohesion Analysis of Japanese Texts
The meaning of natural language text is supported by cohesion among various kinds of entities, including coreference relations, predicate-argument structures, and bridging anaphora relations.
Reverse Operation based Data Augmentation for Solving Math Word Problems
Automatically solving math word problems is a critical task in the field of natural language processing.
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets.
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.
Building a Japanese Typo Dataset from Wikipedia's Revision History
User generated texts contain many typos for which correction is necessary for NLP systems to work.
Development of a Japanese Personality Dictionary based on Psychological Methods
In this study, we collect personality words, using word embeddings, and construct a personality dictionary with weights for Big Five traits.
Acquiring Social Knowledge about Personality and Driving-related Behavior
Using them, we automatically extracted collocations between personality descriptors and driving-related behavior from a driving behavior and subjectivity corpus (1, 803, 328 sentences after filtering) and obtained unique 5, 334 collocations.
Tree-structured Decoding for Solving Math Word Problems
To address this problem, we propose a tree-structured decoding method that generates the abstract syntax tree of the equation in a top-down manner.
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis
To improve the accuracy of predicate-argument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable.
Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction
Typical event sequences are an important class of commonsense knowledge.
Shrinking Japanese Morphological Analyzers With Neural Networks and Semi-supervised Learning
Morphological analyzers are trained on data hand-annotated with segmentation boundaries and part of speech tags.
Juman++: A Morphological Analysis Toolkit for Scriptio Continua
We present a three-part toolkit for developing morphological analyzers for languages without natural word boundaries.
Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion
Considerable effort has been devoted to building commonsense knowledge bases.
Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis
Japanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult.
Knowledge-enriched Two-layered Attention Network for Sentiment Analysis
We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis.
Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis
This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge.
Neural Joint Model for Transition-based Chinese Syntactic Analysis
We present neural network-based joint models for Chinese word segmentation, POS tagging and dependency parsing.
Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames
To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles.
Reading Comprehension using Entity-based Memory Network
This paper introduces a novel neural network model for question answering, the \emph{entity-based memory network}.
SCTB: A Chinese Treebank in Scientific Domain
Treebanks are curial for natural language processing (NLP).
Consistent Word Segmentation, Part-of-Speech Tagging and Dependency Labelling Annotation for Chinese Language
In this paper, we propose a new annotation approach to Chinese word segmentation, part-of-speech (POS) tagging and dependency labelling that aims to overcome the two major issues in traditional morphology-based annotation: Inconsistency and data sparsity.
Large-Scale Acquisition of Commonsense Knowledge via a Quiz Game on a Dialogue System
Commonsense knowledge is essential for fully understanding language in many situations.
A Framework for Compiling High Quality Knowledge Resources From Raw Corpora
The identification of various types of relations is a necessary step to allow computers to understand natural language text.
Single Classifier Approach for Verb Sense Disambiguation based on Generalized Features
In order to overcome this problem, we create a single classifier to be applied to rare or unseen verbs in a new text.
