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prism: MT evaluation in many languages about zero-shot paraphrasing
prism is an automatic MT metric using a sequence-to-sequence paraphrash to achieve MT system outputs conditioned on their respective human references. PRISM uses a multilingual NMT model as a paraphraser null-shot that negates the need for synthetic paraphrase data, and leads to a single model that works in many languages.
prism outperforms or statistically bindings with all metrics that the WMT 2019 metrics have been taken together as a segment level human correlation.
We offer a large, pre-familiar multilingual NMT model that we use as multilingual paraphraser, but the model can also be used for the research community via MT metrics. We offer examples of using the model for multilingual translation and paraphras generation.
prism plays a crude, respectable text; All preprocessing operations are used internally. This document describes how to install and use Prismma.
prism requires a version of Fairseq compatible with the provided pre-free model. We recommend to start with a clean environment:
For adequate speeds we recommend running on a machine with a GPU and the CUDA version that is compatible with the FairSEQ / Torch version installed above. Prisma runs on a GPU, if available; To run instead on the CPU, set CUDA_VISIBLE_DEVICES to a blank string.
Download the PRISM code and install requirements, including Fairseq:
Metric use: command line
Creating test candidates / reference files:
To get the metric values of the system level, Run:
Here, "ref.en" is the (eight) human reference, and "Cand.en" is the (respectable) system output. This command prints some protocol information to STDR, including a model / version identifier, and print the system level score (negative, higher is better) to Stdout:
Candidates can also be conductive in prism.py:If you use the output with the source instead of the reference (d. H. Quality estimation as metric), use the flag of --src. Note that - long still indicates the destination / reference language:
prism also has access to all WMT test sets over the SACREBLEU API. These can be set as arguments to -lc and -ref, for a hypothetical system that outputs $ CAND as follows:
What causes it to use the English reference of the German-English test for WMT19. (Since the language is known, NO-long is required).
To view all options, including segment level scoring, Run:
Metric use: Python module
All functionality is also available in Python, for example:
That should produce:
The prism model is simply a multilingual NMT model and can be used for the translation - see the multilingual Readme.
Attempt to produce paraphrases from the prism model over naive beam search (eg translation of french to French) leads to most time to trivial copies. However, we offer a simple algorithm to discourage copying, and enable the paraphras generation in many languages - see the Paraphrave Generation Readme.
Albanian (sqm), Arabic (AR), Bengali (BN), Bulgarian (BG), Catalan; Valencian (CA), Chinese (ZH), Croatian (HR), Czech (CS), Danish (DA), Dutch (NL), English (s), Esperanto (EO), Estonian (ET), Finnish (FI), French (Fri), German (s), Greek, Modern (El), Hebrew (Modern) (HE), Hungarian (HU), Indonesian (ID), Italian (IT), Japanese (Yes), Kazakhst (KK), Latvian (LV), Lithuanian (LT), Macedonia (MK), Norwegian (NO), Polish (PL), Portuguese (PT), Romanian, Moldavan (RO), Russian (RU), Serbian (SR), Slovak (SK ), Slovene (SL), Spanish; Castilian (ES), Swedish (SV), Turkish (TR), Ukrainian (UK), Vietnamese (VI)
If you quote the prism metric and / or the multilingual NMT model, please:
If you quote the paraphrase generation algorithm, please:
MT evaluation in many languages about zero-shot paraphrasing