1

Rules Based Machine Translation

Fred HollowoodConsultant

RBMT and CL

Sample Agenda

RBMT and CL 2

Introduction1

Rules Based Machine Translation2

Post-Editing3

Quality Measurement4

Controlled Language5

Introduction

The AimBring rapid, cost-effective translation to Symantec’s product and service divisions

Connect Symantec’s CMS to translation technologies

Metrics on the reduction of translation costs and time to market

The Approach

Structure source content so it accommodates MT

Use a language checker to monitor source grammar

Promote terminology as a key process and deliverable

Proactive rather than reactive

Define measures to monitor and drive productivity

GTM, Meteor, BLEU

Work with post-editors to ensure a win-winRBMT and CL 3

Technology Initiative - The Aim

Rules Based Machine Translation

RBMT and CL 4

SL Text

Analysis

SL Lexicon &Grammars

Transfer

SL->TL Lexical &Structural Rules

Synthesis

TL Text

TL Lexicon &Grammars

Flowchart of Rule-Based Machine Translation (RBMT)

MT Process Overview

RBMT and CL 5

Controlled Language Authoring

Automated Pre-processing

User Dictionary

Translation System

Normalisation Dictionary

Automated Post-processing

Human Post-Editing

Systran Engine

Remote Human ActivitySystem Control PhasesText Processing

Post-Editing

Fundamentally same relationship as with traditional vendor

Increased daily throughput expected for Post Edited content (6-8k Vs 2.5k p/day)

Style requirements have been critically reviewed in the light of PE

E.g. stylistic inconsistencies are acceptable for post-edited content

RBMT and CL 6

RBMT and CL 7

Measurement

Metrics based on Comprehensibility

RBMT and CL 8

Score Criteria

Excellent MT output (E) (4)

Read the MT output first. Then read the Source Text (ST). Your understanding of the MT output is not improved by the reading of the ST because the MT output is satisfactory and would not need to be modified.

An end-user who does not have access to the ST would be able to understand the MT output.

Good MT output (G) (3)

Read the MT output first. Then read the source text.

Your understanding of the MT output is not improved by the reading of the ST even though the MT output contains minor grammatical mistakes.

An end-user who does not have access to the source text could possibly understand the MT output.

Medium MT output (M) (2)

Read the MT output first. Then read the source text. Your understanding of the MT output is improved by the reading of the ST, due to significant errors in the MT output.

An end-user who does not have access to the source text could only get the gist of the MT output.

Poor MT output (P) (1)

Read the MT output first. Then read the source text. Your understanding only derives from the reading of the ST, as you could not understand the MT output.

An end-user who does not have access to the source text would not be able to understand the MT output at all.

Quality by Human Inspection

RBMT and CL 9

Hamlet Language Analysis TK 1 - 6

50

98

276

144

122

145

216

111112

141

230

107

41

102

190

267

0

50

100

150

200

250

300

Poor Medium Good Excellent

Spanish

Italian

German

French

GTM Scoring

RBMT and CL 10

From the machine

From the post-editor

Quality Metrics by Language

RBMT and CL 11

Hamlet GTM Results

13.96%

18.92%

20.07%

14.69%

2.93%

18.37%

1.11%0.64%

0.29%

6.39%

2.62%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

0.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-0.99 01:00

French

Spanish

Hamlet GTM Results

0.64%0.29%

1.11%

2.62%

6.39%

13.96%

18.92%

20.07%

14.69%

2.93%

18.37%

0.60% 0.28%1.20%

2.97%

8.87%

18.37%

24.19%

19.54%

12.06%

2.35%

9.57%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

0.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-0.99 01:00

French

Spanish

Hamlet GTM Results

0.64%0.29%

1.11%

2.62%

6.39%

13.96%

18.92%

20.07%

14.69%

2.93%

18.37%

0.60%0.28%

1.20%

2.97%

8.87%

18.37%

24.19%

19.54%

12.06%

2.35%

9.57%

0.97% 0.86%

3.33%

9.42%

17.49%

22.31%

18.82%

12.55%

6.88%

1.25%

6.12%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

0.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-0.99 01:00

French

Spanish

Italian

Hamlet GTM Results

0.64%0.29%

1.11%

2.62%

6.39%

13.96%

18.92%

20.07%

14.69%

2.93%

18.37%

0.60%0.28%

1.20%

2.97%

8.87%

18.37%

24.19%

19.54%

12.06%

2.35%

9.57%

0.97% 0.86%

3.33%

9.42%

17.49%

22.31%

18.82%

12.55%

6.88%

1.25%

6.12%

2.97%2.24%

5.67%

10.85%

16.29%

18.53%

16.14%

11.80%

6.30%

0.99%

8.21%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

0.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-0.99 01:00

French

Spanish

Italian

German

Project Scores by Language

French: 73%

Spanish: 68%

Italian: 59%

German:57%

Example Style rules

Avoid using a colon after a drive letter

Avoid “he”, “she”, “he/she”, and “s/he”

Use numerals for all measurements over 10

Use the serial comma

Do not use more than two adverbs or adjectives in a series

Keep the subject and verb close to each other early in a sentence

Avoid meaningless openers

Avoid progressive tense when describing product use

Do not use future when describing product use

Make positive statements that tell users what to do or what they need to know

Use sentence-style capitalization for bulleted lists

Use a colon at the end of a sentence to introduce a bulleted list

Punctuate imperative sentences in bulleted lists

Use number × number

Use a hyphen in a unit

Repeat the unit of measure

RBMT and CL 12

CL rules based on CDG

Avoid using the passive voice

Do not use more than 25 words in a sentence (original recommendation was 20)

Use relative pronouns

Use complementizers (“that”)

Avoid unnecessary words (such as “basic” or “just”)

Do not use 'this' or 'that' when they are not followed by a noun

Place all non-translatable text on its own line (programming code snippets)

RBMT and CL 13

CL rules for MT

Do not use slashes to list lexical items

Do not write the full name of each operating system

Avoid –ing words

Use a noun at the start of subordinate clause

Repeat the head noun in ambiguous coordinated structures

Use a hyphen to indicate the first part of a compound

Use articles in specific contexts (for disambiguation)

Keep both parts of a two-part verb together

Use "could" with "if“

Avoid parenthetical expressions in the middle of a sentence

RBMT and CL 14

Examples of CL Violation

Keep both parts of a two-part verb together

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Ce document donne des directions à l'analyse du courrier électronique de tour en fonction ou hors fonction.

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Dieses Dokument gibt Richtungen, E-Mail-Prüfung zu aktivieren oder zu deaktivieren.

Ce document donne des directions pour activer ou désactiver l'analyse du courrier électronique.

RBMT and CL 15

Lessons Learned

Strict implementation when there is:

New content

Little leverage

Time

Rules can be context-sensitive

Different results depending on client application

May not always flag tag problems

Language-specific rules

Probably best implemented as:Pre-processing step

Normalization dictionaries

CL + MT is not sufficient

Terminology work to update dictionaries

PE when specific qualify standard is required

RBMT and CL 16

Thank you!

Copyright © 2010 FRED Hollowood CONSULTING . All rights reserved.

This document is provided for informational purposes only and is not intended as advertising. All warranties relating to the information in this document, either express or implied, are disclaimed to the maximum extent allowed by law. The information in this document is subject to change without notice.

RBMT and CL 17

Fred Hollowoodfred@fredhollowoodconsulting.com