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Found 45 papers, 17 papers with code
Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation
Experimental results on both quality estimation of machine translation and cross-lingual semantic textual similarity tasks reveal that our method consistently outperforms the strong baselines using the original multilingual embedding.
Cross-Lingual Semantic Textual Similarity
Machine Translation
+3
IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
We introduce the IDSOU submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets.
Definition Modelling for Appropriate Specificity
Definition generation techniques aim to generate a definition of a target word or phrase given a context.
Distilling Word Meaning in Context from Pre-trained Language Models
The results confirm that our representations exhibited a competitive performance compared to that of the state-of-the-art method transforming contextualised representations for the context-aware lexical semantic tasks and outperformed it for STS estimation.
DIRECT: Direct and Indirect Responses in Conversational Text Corpus
We create a large-scale dialogue corpus that provides pragmatic paraphrases to advance technology for understanding the underlying intentions of users.
Comparing BERT-based Reward Functions for Deep Reinforcement Learning in Machine Translation
In text generation tasks such as machine translation, models are generally trained using cross-entropy loss.
A Japanese Masked Language Model for Academic Domain
We release a pretrained Japanese masked language model for an academic domain.
TMUOU Submission for WMT20 Quality Estimation Shared Task
We introduce the TMUOU submission for the WMT20 Quality Estimation Shared Task 1: Sentence-Level Direct Assessment.
TMEKU System for the WAT2021 Multimodal Translation Task
We introduce our TMEKU system submitted to the English-Japanese Multimodal Translation Task for WAT 2021.
Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation
We propose a method to distill language-agnostic meaning embeddings from multilingual sentence encoders for unsupervised quality estimation of machine translation.
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain
We annotate 35, 000 SNS posts with both the writer’s subjective sentiment polarity labels and the reader’s objective ones to construct a Japanese sentiment analysis dataset.
JADE: Corpus for Japanese Definition Modelling
This study investigated and released the JADE, a corpus for Japanese definition modelling, which is a technique that automatically generates definitions of a given target word and phrase.
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation
Therefore, we create a benchmark test dataset for Japanese-to-English MLCC-MT from the Newsela corpus by introducing an automatic filtering of data with inappropriate sentence-level complexity, manual check for parallel target language sentences with different complexity levels, and manual translation.
Unsupervised Translation Quality Estimation Exploiting Synthetic Data and Pre-trained Multilingual Encoder
In this paper, we extensively investigate the usefulness of synthetic TQE data and pre-trained multilingual encoders in unsupervised sentence-level TQE, both of which have been proven effective in the supervised training scenarios.
CEFR-Based Sentence Difficulty Annotation and Assessment
Controllable text simplification is a crucial assistive technique for language learning and teaching.
Distinct Label Representations for Few-Shot Text Classification
Few-shot text classification aims to classify inputs whose label has only a few examples.
Edit Distance Based Curriculum Learning for Paraphrase Generation
Curriculum learning has improved the quality of neural machine translation, where only source-side features are considered in the metrics to determine the difficulty of translation.
WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations
We annotate 17, 000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset.
SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction
We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC).
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity
We optimize rewards of reinforcement learning in text simplification using metrics that are highly correlated with human-perspectives.
Tiny Word Embeddings Using Globally Informed Reconstruction
We reduce the model size of pre-trained word embeddings by a factor of 200 while preserving its quality.
Double Attention-based Multimodal Neural Machine Translation with Semantic Image Regions
In contrast, we propose the application of semantic image regions for MNMT by integrating visual and textual features using two individual attention mechanisms (double attention).
Text Classification with Negative Supervision
Advanced pre-trained models for text representation have achieved state-of-the-art performance on various text classification tasks.
Annotation of Adverse Drug Reactions in Patients' Weblogs
The dataset we present in this paper is unique for the richness of annotated information, including detailed descriptions of drug reactions with full context.
SAPPHIRE: Simple Aligner for Phrasal Paraphrase with Hierarchical Representation
To estimate the likelihood of phrase alignments, SAPPHIRE uses phrase embeddings that are hierarchically composed of word embeddings.
Natural Language Inference
Natural Language Understanding
+2
Word Complexity Estimation for Japanese Lexical Simplification
We introduce three language resources for Japanese lexical simplification: 1) a large-scale word complexity lexicon, 2) the first synonym lexicon for converting complex words to simpler ones, and 3) the first toolkit for developing and benchmarking Japanese lexical simplification system.
Contextualized context2vec
Herein we propose a method that combines these two approaches to contextualize word embeddings for lexical substitution.
Machine Translation Evaluation with BERT Regressor
We introduce the metric using BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) for automatic machine translation evaluation.
Controllable Text Simplification with Lexical Constraint Loss
Our text simplification method succeeds in translating an input into a specific grade level by considering levels of both sentences and words.
Negative Lexically Constrained Decoding for Paraphrase Generation
Paraphrase generation can be regarded as monolingual translation.
Using Natural Language Processing to Develop an Automated Orthodontic Diagnostic System
We work on the task of automatically designing a treatment plan from the findings included in the medical certificate written by the dentist.
RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation
We introduce the RUSE metric for the WMT18 metrics shared task.
Complex Word Identification Based on Frequency in a Learner Corpus
We introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018.
TMU System for SLAM-2018
We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018).
Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams.
Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language
Neural machine translation (NMT) produces sentences that are more fluent than those produced by statistical machine translation (SMT).
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification
This paper examines the usefulness of semantic features based on word alignments for estimating the quality of text simplification.
MIPA: Mutual Information Based Paraphrase Acquisition via Bilingual Pivoting
We present a pointwise mutual information (PMI)-based approach to formalize paraphrasability and propose a variant of PMI, called MIPA, for the paraphrase acquisition.
Building a Monolingual Parallel Corpus for Text Simplification Using Sentence Similarity Based on Alignment between Word Embeddings
To obviate the need for human annotation, we propose an unsupervised method that automatically builds the monolingual parallel corpus for text simplification using sentence similarity based on word embeddings.
