67,435 research outputs found
Stream-based Translation Models for Statistical Machine Translation
Typical statistical machine translation systems are trained with static parallel corpora. Here we account for scenarios with a continuous incoming stream of parallel training data. Such scenarios include daily governmental proceedings, sustained output from translation agencies, or crowd-sourced translations. We show incorporating recent sentence pairs from the stream improves performance compared with a static baseline. Since frequent batch retraining is computationally demanding we introduce a fast incremental alternative using an online version of the EM algorithm. To bound our memory requirements we use a novel data-structure and associated training regime. When compared to frequent batch retraining, our online time and space-bounded model achieves the same performance with significantly less computational overhead.
Domain adaptation strategies in statistical machine translation: a brief overview
© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft
Learning Semantic Correspondences in Technical Documentation
We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representations in the target documentation, such as formal representation of
functions or code templates. Our approach exploits the parallel nature of such
documentation, or the tight coupling between high-level text and the low-level
representations we aim to learn. Data is collected by mining technical
documents for such parallel text-representation pairs, which we use to train a
simple semantic parsing model. We report new baseline results on sixteen novel
datasets, including the standard library documentation for nine popular
programming languages across seven natural languages, and a small collection of
Unix utility manuals.Comment: accepted to ACL-201
The ethics of machine translation
In this paper I first describe the two main branches in machine translation research. I then go to discuss why the second of these, statistical machine translation, can cause some malaise among translation scholars. As some of the issues that arise are ethical in nature, I stop to ponder what an ethics of machine translation might involve, before considering the ethical stance adopted by some of the main protagonists in the development and popularisation of statistical machine translation, and in the teaching of translation
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning.
Driven by ML applications, the number of different ML inference systems has
exploded. Over 100 organizations are building ML inference chips, and the
systems that incorporate existing models span at least three orders of
magnitude in power consumption and five orders of magnitude in performance;
they range from embedded devices to data-center solutions. Fueling the hardware
are a dozen or more software frameworks and libraries. The myriad combinations
of ML hardware and ML software make assessing ML-system performance in an
architecture-neutral, representative, and reproducible manner challenging.
There is a clear need for industry-wide standard ML benchmarking and evaluation
criteria. MLPerf Inference answers that call. In this paper, we present our
benchmarking method for evaluating ML inference systems. Driven by more than 30
organizations as well as more than 200 ML engineers and practitioners, MLPerf
prescribes a set of rules and best practices to ensure comparability across
systems with wildly differing architectures. The first call for submissions
garnered more than 600 reproducible inference-performance measurements from 14
organizations, representing over 30 systems that showcase a wide range of
capabilities. The submissions attest to the benchmark's flexibility and
adaptability.Comment: ISCA 202
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