2 research outputs found
Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
Sentence representations can capture a wide range of information that cannot
be captured by local features based on character or word N-grams. This paper
examines the usefulness of universal sentence representations for evaluating
the quality of machine translation. Although it is difficult to train sentence
representations using small-scale translation datasets with manual evaluation,
sentence representations trained from large-scale data in other tasks can
improve the automatic evaluation of machine translation. Experimental results
of the WMT-2016 dataset show that the proposed method achieves state-of-the-art
performance with sentence representation features only.Comment: NAACL 2018 Student Research Workshop; 6 page
Machine Translation Evaluation with Neural Networks
We present a framework for machine translation evaluation using neural
networks in a pairwise setting, where the goal is to select the better
translation from a pair of hypotheses, given the reference translation. In this
framework, lexical, syntactic and semantic information from the reference and
the two hypotheses is embedded into compact distributed vector representations,
and fed into a multi-layer neural network that models nonlinear interactions
between each of the hypotheses and the reference, as well as between the two
hypotheses. We experiment with the benchmark datasets from the WMT Metrics
shared task, on which we obtain the best results published so far, with the
basic network configuration. We also perform a series of experiments to analyze
and understand the contribution of the different components of the network. We
evaluate variants and extensions, including fine-tuning of the semantic
embeddings, and sentence-based representations modeled with convolutional and
recurrent neural networks. In summary, the proposed framework is flexible and
generalizable, allows for efficient learning and scoring, and provides an MT
evaluation metric that correlates with human judgments, and is on par with the
state of the art.Comment: Machine Translation, Reference-based MT Evaluation, Deep Neural
Networks, Distributed Representation of Texts, Textual Similarit