31,705 research outputs found
Findings of the Third Workshop on Neural Generation and Translation
This document describes the findings of the Third Workshop on Neural
Generation and Translation, held in concert with the annual conference of the
Empirical Methods in Natural Language Processing (EMNLP 2019). First, we
summarize the research trends of papers presented in the proceedings. Second,
we describe the results of the two shared tasks 1) efficient neural machine
translation (NMT) where participants were tasked with creating NMT systems that
are both accurate and efficient, and 2) document-level generation and
translation (DGT) where participants were tasked with developing systems that
generate summaries from structured data, potentially with assistance from text
in another language.Comment: Fixed the metadata (author list
Referenceless Quality Estimation for Natural Language Generation
Traditional automatic evaluation measures for natural language generation
(NLG) use costly human-authored references to estimate the quality of a system
output. In this paper, we propose a referenceless quality estimation (QE)
approach based on recurrent neural networks, which predicts a quality score for
a NLG system output by comparing it to the source meaning representation only.
Our method outperforms traditional metrics and a constant baseline in most
respects; we also show that synthetic data helps to increase correlation
results by 21% compared to the base system. Our results are comparable to
results obtained in similar QE tasks despite the more challenging setting.Comment: Accepted as a regular paper to 1st Workshop on Learning to Generate
Natural Language (LGNL), Sydney, 10 August 201
Multilingual Unsupervised Sentence Simplification
Progress in Sentence Simplification has been hindered by the lack of
supervised data, particularly in languages other than English. Previous work
has aligned sentences from original and simplified corpora such as English
Wikipedia and Simple English Wikipedia, but this limits corpus size, domain,
and language. In this work, we propose using unsupervised mining techniques to
automatically create training corpora for simplification in multiple languages
from raw Common Crawl web data. When coupled with a controllable generation
mechanism that can flexibly adjust attributes such as length and lexical
complexity, these mined paraphrase corpora can be used to train simplification
systems in any language. We further incorporate multilingual unsupervised
pretraining methods to create even stronger models and show that by training on
mined data rather than supervised corpora, we outperform the previous best
results. We evaluate our approach on English, French, and Spanish
simplification benchmarks and reach state-of-the-art performance with a totally
unsupervised approach. We will release our models and code to mine the data in
any language included in Common Crawl
Neural Machine Translation into Language Varieties
Both research and commercial machine translation have so far neglected the
importance of properly handling the spelling, lexical and grammar divergences
occurring among language varieties. Notable cases are standard national
varieties such as Brazilian and European Portuguese, and Canadian and European
French, which popular online machine translation services are not keeping
distinct. We show that an evident side effect of modeling such varieties as
unique classes is the generation of inconsistent translations. In this work, we
investigate the problem of training neural machine translation from English to
specific pairs of language varieties, assuming both labeled and unlabeled
parallel texts, and low-resource conditions. We report experiments from English
to two pairs of dialects, EuropeanBrazilian Portuguese and European-Canadian
French, and two pairs of standardized varieties, Croatian-Serbian and
Indonesian-Malay. We show significant BLEU score improvements over baseline
systems when translation into similar languages is learned as a multilingual
task with shared representations.Comment: Published at EMNLP 2018: third conference on machine translation (WMT
2018
Machine translation evaluation resources and methods: a survey
We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT.
This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content
Latent Variable Model for Multi-modal Translation
In this work, we propose to model the interaction between visual and textual
features for multi-modal neural machine translation (MMT) through a latent
variable model. This latent variable can be seen as a multi-modal stochastic
embedding of an image and its description in a foreign language. It is used in
a target-language decoder and also to predict image features. Importantly, our
model formulation utilises visual and textual inputs during training but does
not require that images be available at test time. We show that our latent
variable MMT formulation improves considerably over strong baselines, including
a multi-task learning approach (Elliott and K\'ad\'ar, 2017) and a conditional
variational auto-encoder approach (Toyama et al., 2016). Finally, we show
improvements due to (i) predicting image features in addition to only
conditioning on them, (ii) imposing a constraint on the minimum amount of
information encoded in the latent variable, and (iii) by training on additional
target-language image descriptions (i.e. synthetic data).Comment: Paper accepted at ACL 2019. Contains 8 pages (11 including
references, 13 including appendix), 6 figure
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