Жапон деректер жиынтығы

material ID: japanesedataset

Related Papers

Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractWe propose new BERT-based methods for recognizing event causality such as “smoke cigarettes” –> “die of lung cancer” written in web texts. In our methods, we grasp each annotator’s policy …...

8927

End-to-end ASR to jointly predict transcriptions and linguistic annotations

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractWe propose a Transformer-based sequence-to-sequence model for automatic speech recognition (ASR) capable of simultaneously transcribing and annotating audio with linguistic information such as phonemic transcripts or part-of-speech (POS) tags. Since …...

21725

A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThere has been significant progress in the field of sentiment analysis. However, aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language even though it has a huge …...

14255

Leveraging Three Types of Embeddings from Masked Language Models in Idiom Token Classification

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractMany linguistic expressions have idiomatic and literal interpretations, and the automatic distinction of these two interpretations has been studied for decades. Recent research has shown that contextualized word embeddings derived …...

26505

Semantic Shift Stability: Efficient Way to Detect Performance Degradation of Word Embeddings and Pre-trained Language Models

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractWord embeddings and pre-trained language models have become essential technical elements in natural language processing. While the general practice is to use or fine-tune publicly available models, there are significant …...

632

Lexical Complexity Prediction using Word Embeddings

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

Lexical complexity is crucial for reading comprehension. In the past, research work of lexical complexity prediction mainly focuses on differentiating the complexity difference between two words. Moreover, most of the …...

1103