752 research outputs found
Results of the WMT19 metrics shared task: segment-level and strong MT systems pose big challenges
This paper presents the results of the WMT19 Metrics Shared Task. Participants were asked to score the outputs of the translations systems competing in the WMT19 News Translation Task with automatic metrics. 13 research groups submitted 24 metrics, 10 of which are reference-less "metrics" and constitute submissions to the joint task with WMT19 Quality Estimation Task, "QE as a Metric". In addition, we computed 11 baseline metrics, with 8 commonly applied baselines (BLEU, SentBLEU, NIST, WER, PER, TER, CDER, and chrF) and 3 reimplementations (chrF+, sacreBLEU-BLEU, and sacreBLEU-chrF). Metrics were evaluated on the system level, how well a given metric correlates with the WMT19 official manual ranking, and segment level, how well the metric correlates with human judgements of segment quality. This year, we use direct assessment (DA) as our only form of manual evaluation
Large-scale Hierarchical Alignment for Data-driven Text Rewriting
We propose a simple unsupervised method for extracting pseudo-parallel
monolingual sentence pairs from comparable corpora representative of two
different text styles, such as news articles and scientific papers. Our
approach does not require a seed parallel corpus, but instead relies solely on
hierarchical search over pre-trained embeddings of documents and sentences. We
demonstrate the effectiveness of our method through automatic and extrinsic
evaluation on text simplification from the normal to the Simple Wikipedia. We
show that pseudo-parallel sentences extracted with our method not only
supplement existing parallel data, but can even lead to competitive performance
on their own.Comment: RANLP 201
A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing
We investigate a long-perceived shortcoming in the typical use of BLEU: its
reliance on a single reference. Using modern neural paraphrasing techniques, we
study whether automatically generating additional diverse references can
provide better coverage of the space of valid translations and thereby improve
its correlation with human judgments. Our experiments on the into-English
language directions of the WMT19 metrics task (at both the system and sentence
level) show that using paraphrased references does generally improve BLEU, and
when it does, the more diverse the better. However, we also show that better
results could be achieved if those paraphrases were to specifically target the
parts of the space most relevant to the MT outputs being evaluated. Moreover,
the gains remain slight even when human paraphrases are used, suggesting
inherent limitations to BLEU's capacity to correctly exploit multiple
references. Surprisingly, we also find that adequacy appears to be less
important, as shown by the high results of a strong sampling approach, which
even beats human paraphrases when used with sentence-level BLEU.Comment: Accepted in the Findings of EMNLP 202
Deep Learning for Text Style Transfer: A Survey
Text style transfer is an important task in natural language generation,
which aims to control certain attributes in the generated text, such as
politeness, emotion, humor, and many others. It has a long history in the field
of natural language processing, and recently has re-gained significant
attention thanks to the promising performance brought by deep neural models. In
this paper, we present a systematic survey of the research on neural text style
transfer, spanning over 100 representative articles since the first neural text
style transfer work in 2017. We discuss the task formulation, existing datasets
and subtasks, evaluation, as well as the rich methodologies in the presence of
parallel and non-parallel data. We also provide discussions on a variety of
important topics regarding the future development of this task. Our curated
paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_SurveyComment: Computational Linguistics Journal 202
Trained MT Metrics Learn to Cope with Machine-translated References
Neural metrics trained on human evaluations of MT tend to correlate well with
human judgments, but their behavior is not fully understood. In this paper, we
perform a controlled experiment and compare a baseline metric that has not been
trained on human evaluations (Prism) to a trained version of the same metric
(Prism+FT). Surprisingly, we find that Prism+FT becomes more robust to
machine-translated references, which are a notorious problem in MT evaluation.
This suggests that the effects of metric training go beyond the intended effect
of improving overall correlation with human judgments.Comment: WMT 202
Proceedings of the 17th Annual Conference of the European Association for Machine Translation
Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT
Trained MT Metrics Learn to Cope with Machine-translated References
Neural metrics trained on human evaluations of MT tend to correlate well with human judgments, but their behavior is not fully understood. In this paper, we perform a controlled experiment and compare a baseline metric that has not been trained on human evaluations (Prism) to a trained version of the same metric (Prism+FT). Surprisingly, we find that Prism+FT becomes more robust to machine-translated references, which are a notorious problem in MT evaluation. This suggests that the effects of metric training go beyond the intended effect of improving overall correlation with human judgments
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
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