Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation

SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models. In this paper, we describe three use cases in which SGNMT is currently playing an active role: (1) teaching as SGNMT is being used for course work and student theses in the MPhil in Machine Learning, Speech and Language Technology at the University of Cambridge, (2) research as most of the research work of the Cambridge MT group is based on SGNMT, and (3) technology transfer as we show how SGNMT is helping to transfer research findings from the laboratory to the industry, eg. into a product of SDL plc

Unfolding and Shrinking Neural Machine Translation Ensembles

Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance. Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On Japanese-English we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC grant EP/L027623/1)

Source sentence simplification for statistical machine translation

Long sentences with complex syntax and long-distance dependencies pose difficulties for machine translation systems. Short sentences, on the other hand, are usually easier to translate. We study the potential of addressing this mismatch using text simplifi- cation: given a simplified version of the full input sentence, can we use it in addition to the full input to improve translation? We show that the spaces of original and simplified translations can be effectively combined using translation lattices and compare two decoding approaches to process both inputs at different levels of integration. We demonstrate on source-annotated portions of WMT test sets and on top of strong baseline systems combining hierarchical and neural translation for two language pairs that source simplification can help to improve translation quality.This work was supported by the EPSRC grant Improving Target Language Fluency in Statistical Machine Translation, grant number EP/L027623/1

Product assurance technology for custom LSI/VLSI electronics

The technology for obtaining custom integrated circuits from CMOS-bulk silicon foundries using a universal set of layout rules is presented. The technical efforts were guided by the requirement to develop a 3 micron CMOS test chip for the Combined Release and Radiation Effects Satellite (CRRES). This chip contains both analog and digital circuits. The development employed all the elements required to obtain custom circuits from silicon foundries, including circuit design, foundry interfacing, circuit test, and circuit qualification

SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies

This paper introduces SGNMT, our experimental platform for machine translation research. SGNMT provides a generic interface to neural and symbolic scoring modules (predictors) with left-to-right semantic such as translation models like NMT, language models, translation lattices, n-best lists or other kinds of scores and constraints. Predictors can be combined with other predictors to form complex decoding tasks. SGNMT implements a number of search strategies for traversing the space spanned by the predictors which are appropriate for different predictor constellations. Adding new predictors or decoding strategies is particularly easy, making it a very efficient tool for prototyping new research ideas. SGNMT is actively being used by students in the MPhil program in Machine Learning, Speech and Language Technology at the University of Cambridge for course work and theses, as well as for most of the research work in our group.This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC grant EP/L027623/1)

Syntactically Guided Neural Machine Translation

We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT). Weight pushing transforms the Hiero scores for complete translation hypotheses, with the full translation grammar score and full n-gram language model score, into posteriors compatible with NMT predictive probabilities. With a slightly modified NMT beam-search decoder we find gains over both Hiero and NMT decoding alone, with practical advantages in extending NMT to very large input and output vocabularies.Engineering and Physical Sciences Research Council (Grant ID: EP/L027623/1

Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices

We present a novel scheme to combine neural machine translation (NMT) with traditional statistical machine translation (SMT). Our approach borrows ideas from linearised lattice minimum Bayes-risk decoding for SMT. The NMT score is combined with the Bayes-risk of the translation according the SMT lattice. This makes our approach much more flexible than n-best list or lattice rescoring as the neural decoder is not restricted to the SMT search space. We show an efficient and simple way to integrate risk estimation into the NMT decoder which is suitable for word-level as well as subword-unit-level NMT. We test our method on English-German and Japanese-English and report significant gains over lattice rescoring on several data sets for both single and ensembled NMT. The MBR decoder produces entirely new hypotheses far beyond simply rescoring the SMT search space or fixing UNKs in the NMT output.This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC grant EP/L027623/1)

Cryo-EM structures and binding of mouse and human ACE2 to SARS-CoV-2 variants of concern indicate that mutations enabling immune escape could expand host range.

Investigation of potential hosts of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is crucial to understanding future risks of spillover and spillback. SARS-CoV-2 has been reported to be transmitted from humans to various animals after requiring relatively few mutations. There is significant interest in describing how the virus interacts with mice as they are well adapted to human environments, are used widely as infection models and can be infected. Structural and binding data of the mouse ACE2 receptor with the Spike protein of newly identified SARS-CoV-2 variants are needed to better understand the impact of immune system evading mutations present in variants of concern (VOC). Previous studies have developed mouse-adapted variants and identified residues critical for binding to heterologous ACE2 receptors. Here we report the cryo-EM structures of mouse ACE2 bound to trimeric Spike ectodomains of four different VOC: Beta, Omicron BA.1, Omicron BA.2.12.1 and Omicron BA.4/5. These variants represent the oldest to the newest variants known to bind the mouse ACE2 receptor. Our high-resolution structural data complemented with bio-layer interferometry (BLI) binding assays reveal a requirement for a combination of mutations in the Spike protein that enable binding to the mouse ACE2 receptor

Limits on Dark Matter Effective Field Theory Parameters with CRESST-II

CRESST is a direct dark matter search experiment, aiming for an observation of nuclear recoils induced by the interaction of dark matter particles with cryogenic scintillating calcium tungstate crystals. Instead of confining ourselves to standard spin-independent and spin-dependent searches, we re-analyze data from CRESST-II using a more general effective field theory (EFT) framework. On many of the EFT coupling constants, improved exclusion limits in the low-mass region (< 3-4 GeV) are presented.Comment: 7 pages, 9 figure

Results on MeV-scale dark matter from a gram-scale cryogenic calorimeter operated above ground

Models for light dark matter particles with masses below 1 GeV/c$^2$ are a natural and well-motivated alternative to so-far unobserved weakly interacting massive particles. Gram-scale cryogenic calorimeters provide the required detector performance to detect these particles and extend the direct dark matter search program of CRESST. A prototype 0.5 g sapphire detector developed for the $\nu$-cleus experiment has achieved an energy threshold of $E_{th}=(19.7\pm 0.9)$ eV, which is one order of magnitude lower than previous results and independent of the type of particle interaction. The result presented here is obtained in a setup above ground without significant shielding against ambient and cosmogenic radiation. Although operated in a high-background environment, the detector probes a new range of light-mass dark matter particles previously not accessible by direct searches. We report the first limit on the spin-independent dark matter particle-nucleon cross section for masses between 140 MeV/c$^2$ and 500 MeV/c$^2$.Comment: 6 pages, 6 figures, v3: ancillary files added, v4: high energy spectrum (0.6-12keV) added to ancillary file