21 citations · 57 across the 16 of their papers we have counts for
17 papers
WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction
Qiyu Wu, Masaaki Nagata, Yoshimasa Tsuruoka
Most existing word alignment methods rely on manual alignment datasets or parallel corpora, which limits their usefulness. Here, to mitigate the dependence on manual data, we broad…
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito +2
To promote and further develop RST-style discourse parsing models, we need a strong baseline that can be regarded as a reference for reporting reliable experimental results. This p…
Domain Adaptation of Machine Translation with Crowdworkers
Makoto Morishita, Jun Suzuki, Masaaki Nagata
Although a machine translation model trained with a large in-domain parallel corpus achieves remarkable results, it still works poorly when no in-domain data are available. This si…
Extending Word-Level Quality Estimation for Post-Editing Assistance
Yizhen Wei, Takehito Utsuro, Masaaki Nagata
We define a novel concept called extended word alignment in order to improve post-editing assistance efficiency. Based on extended word alignment, we further propose a novel task c…
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus
Makoto Morishita, Katsuki Chousa, Jun Suzuki +1
Most current machine translation models are mainly trained with parallel corpora, and their translation accuracy largely depends on the quality and quantity of the corpora. Althoug…
Bilingual Text Extraction as Reading Comprehension
Katsuki Chousa, Masaaki Nagata, Masaaki Nishino
In this paper, we propose a method to extract bilingual texts automatically from noisy parallel corpora by framing the problem as a token-level span prediction, such as SQuAD-style…
A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT
Masaaki Nagata, Chousa Katsuki, Masaaki Nishino
We present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions…
JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus
Makoto Morishita, Jun Suzuki, Masaaki Nagata
Recent machine translation algorithms mainly rely on parallel corpora. However, since the availability of parallel corpora remains limited, only some resource-rich language pairs c…
NTT's Machine Translation Systems for WMT19 Robustness Task
Soichiro Murakami, Makoto Morishita, Tsutomu Hirao +1
This paper describes NTT's submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulti…
Character n-gram Embeddings to Improve RNN Language Models
Sho Takase, Jun Suzuki, Masaaki Nagata
This paper proposes a novel Recurrent Neural Network (RNN) language model that takes advantage of character information. We focus on character n-grams based on research in the fiel…
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction
Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata +4
Question answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop…
Direct Output Connection for a High-Rank Language Model
Sho Takase, Jun Suzuki, Masaaki Nagata
This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also from…
Source-side Prediction for Neural Headline Generation
Shun Kiyono, Sho Takase, Jun Suzuki +3
The encoder-decoder model is widely used in natural language generation tasks. However, the model sometimes suffers from repeated redundant generation, misses important phrases, an…
Input-to-Output Gate to Improve RNN Language Models
Sho Takase, Jun Suzuki, Masaaki Nagata
This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Outp…
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Jun Suzuki, Masaaki Nagata
This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound…
Reading Comprehension using Entity-based Memory Network
Xun Wang, Katsuhito Sudoh, Masaaki Nagata +3
This paper introduces a novel neural network model for question answering, the \emph{entity-based memory network}. It enhances neural networks' ability of representing and calculat…
Enumeration of Extractive Oracle Summaries
Tsutomu Hirao, Masaaki Nishino, Jun Suzuki +1
To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extract…