7,595 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
Automatic evaluation of generation and parsing for machine translation with automatically acquired transfer rules
This paper presents a new method of evaluation for generation and parsing components of transfer-based MT systems where the transfer rules have been automatically
acquired from parsed sentence-aligned bitext corpora. The method provides a means of quantifying the upper bound imposed on the MT system by the quality of the parsing
and generation technologies for the target language. We include experiments to calculate this upper bound for both handcrafted and automatically induced parsing and generation technologies currently in use by transfer-based MT systems
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
We propose a method to transfer knowledge across neural machine translation
(NMT) models by means of a shared dynamic vocabulary. Our approach allows to
extend an initial model for a given language pair to cover new languages by
adapting its vocabulary as long as new data become available (i.e., introducing
new vocabulary items if they are not included in the initial model). The
parameter transfer mechanism is evaluated in two scenarios: i) to adapt a
trained single language NMT system to work with a new language pair and ii) to
continuously add new language pairs to grow to a multilingual NMT system. In
both the scenarios our goal is to improve the translation performance, while
minimizing the training convergence time. Preliminary experiments spanning five
languages with different training data sizes (i.e., 5k and 50k parallel
sentences) show a significant performance gain ranging from +3.85 up to +13.63
BLEU in different language directions. Moreover, when compared with training an
NMT model from scratch, our transfer-learning approach allows us to reach
higher performance after training up to 4% of the total training steps.Comment: Published at the International Workshop on Spoken Language
Translation (IWSLT), 201
Comparative Analysis of the Saccharomyces cerevisiae and Caenorhabditis elegans Protein Interaction Network
Protein interaction networks aim to summarize the complex interplay of
proteins in an organism. Early studies suggested that the position of a protein
in the network determines its evolutionary rate but there has been considerable
disagreement as to what extent other factors, such as protein abundance, modify
this reported dependence.
We compare the genomes of Saccharomyces cerevisiae and Caenorhabditis elegans
with those of closely related species to elucidate the recent evolutionary
history of their respective protein interaction networks. Interaction and
expression data are studied in the light of a detailed phylogenetic analysis.
The underlying network structure is incorporated explicitly into the
statistical analysis.
The increased phylogenetic resolution, paired with high-quality interaction
data, allows us to resolve the way in which protein interaction network
structure and abundance of proteins affect the evolutionary rate. We find that
expression levels are better predictors of the evolutionary rate than a
protein's connectivity. Detailed analysis of the two organisms also shows that
the evolutionary rates of interacting proteins are not sufficiently similar to
be mutually predictive.
It appears that meaningful inferences about the evolution of protein
interaction networks require comparative analysis of reasonably closely related
species. The signature of protein evolution is shaped by a protein's abundance
in the organism and its function and the biological process it is involved in.
Its position in the interaction networks and its connectivity may modulate this
but they appear to have only minor influence on a protein's evolutionary rate.Comment: Accepted for publication in BMC Evolutionary Biolog
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