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Found 21 papers, 3 papers with code
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.
Utterance Position-Aware Dialogue Act Recognition
This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance’s absolute or relative position.
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.
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.
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.
Synchronous Syntactic Attention for Transformer Neural Machine Translation
This paper proposes a novel attention mechanism for Transformer Neural Machine Translation, {``}Synchronous Syntactic Attention,{''} inspired by synchronous dependency grammars.
Hie-BART: Document Summarization with Hierarchical BART
This paper proposes a new abstractive document summarization model, hierarchical BART (Hie-BART), which captures hierarchical structures of a document (i. e., sentence-word structures) in the BART model.
Ranked #7 on
Document Summarization
on CNN / Daily Mail
Neural Text Generation with Artificial Negative Examples
In this paper, we propose to suppress an arbitrary type of errors by training the text generation model in a reinforcement learning framework, where we use a trainable reward function that is capable of discriminating between references and sentences containing the targeted type of errors.
Supervised Visual Attention for Multimodal Neural Machine Translation
This paper proposed a supervised visual attention mechanism for multimodal neural machine translation (MNMT), trained with constraints based on manual alignments between words in a sentence and their corresponding regions of an image.
Bilingual Subword Segmentation for Neural Machine Translation
This paper proposed a new subword segmentation method for neural machine translation, {``}Bilingual Subword Segmentation,{''} which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation.
Transformer-based Approach for Predicting Chemical Compound Structures
Instead of handcrafted rules, we propose Transformer-based models that predict SMILES strings from chemical compound names.
A Visually-Grounded Parallel Corpus with Phrase-to-Region Linking
To verify our dataset, we performed phrase localization experiments in both languages and investigated the effectiveness of our Japanese annotations as well as multilingual learning realized by our dataset.
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing
We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical com- pound paraphrase model.
Dependency-Based Relative Positional Encoding for Transformer NMT
This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism.
Dependency-Based Self-Attention for Transformer NMT
In this paper, we propose a new Transformer neural machine translation (NMT) model that incorporates dependency relations into self-attention on both source and target sides, dependency-based self-attention.
Neural Machine Translation Incorporating Named Entity
To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word.
CKY-based Convolutional Attention for Neural Machine Translation
This paper proposes a new attention mechanism for neural machine translation (NMT) based on convolutional neural networks (CNNs), which is inspired by the CKY algorithm.
