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Live Text Access
Training for real-time intralingual subtitlers.
Unit 2. Linguistic competence
Element 1. Functionality: accuracy, readability and legibility
Accuracy assessment models created by UAB, SSLM and SDI
The learning outcomes for this video lecture
are to produce accurate transcriptions
in terms of spelling, grammar and meaning
and to produce legible and readable transcriptions
both while creating the transcriptions and after the live situation
by applying readability and eligibility indicators
Agenda
in this video lecture we will be dealing with the following points:
From quantity to quality.
Accuracy key areas. Accuracy assessment models.
and we will finish with a Summary
From quantity to quality.
according to the EFHOH
"it is well and good having subtitles
but if they are not accurate
then they are of little or no use to the consumer".
This is a particular problem with regards to live subtitling
Accuracy key areas
There are three key areas
which will determine the accuracy of a real-time subtitle
lexical, means correct pronunciation and/or spelling
grammar, means correct syntactic structure
of the unit
and semantic, means that the meaning of the source text is conveyed.
Errors in these elements can be due to a human failure
or due to a malfunctioning of the software
and they may or may not affect
the understanding of the meaning the speaker intends to deliver
Accuracy assessment models
to measure accuracy there are two main models
the NER model based on the Word Error Rate System
and the IRA model which is a conceptual system.
The NER model is especially effective
when measuring the accuracy of verbatim subtitles
because it's based on a World Error Rate system
in which all utterances of the speaker are respoken or typed.
Therefore the source text from the speaker can be compared
with the output text of the subtitles
The NER model, developed by Romero-Fresco in 2011
is a model to assess the accuracy
by analysing the extent to which errors
affect the coherence of the subtitle text
or modify the content.
In the NER model accuracy is measured following a formula
in which N is the total number of words in the subtitles
E are the addition errors and R the recognition errors.
So N the total number of words of the subtitles
minus E edition errors minus R recognition errors
will be divided by N and um multiplied by 100
This will give us an accuracy rate
saying how accurate the subtitles are
but this is only an indication of the quality.
The final rate should not be taken as an end result.
There is also a section
where correct additions can be included
and an assessment can be made.
in any case the target should not be less than 98%.
This is the established threshold for subtitles
that present an acceptable quality
this model is being used and can be used
in any type of subtitling techniques and working context
as it is based on a word edition.
The IRA model is the idea units rendition assessment model
ideas are any sentence type conveying a meaning
regardless of its degree of synthetic dependency
idea units are divided in rendered and non-rendered idea units
rendered idea units are those represented in the output text
while non-rendered units are those in which a message is not conveyed
due to omission of the unit itself.
Non-rendered idea units are omissions
which occur when the software fails to reproduce a conceptual unit.
Misrepresentations are due to semantic errors
which compromise the decoding of the source text.
Rendered units are repetitions
which means units which are highly comprehensible
due to its faithful verbatim reproduction of the words
added in the search text
and alterations which means that the unit contains minor errors
which however do not deteriorate the comprehensibility of the message
for the end user
there are two types of alterations expansions and reductions.
Expansion occurs when a conceptual unit is explained or disambiguated
using a higher number of characters than those used in the source text.
Reduction occurs when a unit is characterised
by partial omission and/or partial or total reformulation
there are two kinds of reduction, omission and compression
Reduction of the original message is necessary for two main reasons
first to adapt the message to the reading speed of the viewers
and secondly to avoid delay.
If we center the attention to alterations produced by reductions
real time subtitlers should develop editing skills to reduce the message
without losing key information.
Reduction relies on two main strategies
omission which involves deleting redundant information
and compression which involves rewording in a shorter form.
Omission is possible when the audience
is supposed to have prior knowledge of the topic
or when a speaker uses redundant words.
Main types of omission.
According to Romero-Fresco the main types of words
omitted by live subtitlers in the case of redundant words
are discourse markers: so, well, I mean, you know.
connectors: and, but, though
This means that respoken sentences are notably shorter
than the source text sentences
given that the absence of these conjunctions
often entail the beginning of a new sentence.
Also intensifiers: really, much more, well
or repetitions and unimportant asides:
"you were saying" or "worth bearing in mind".
Compression is possible when a word or a group of words
can be substituted by a shorter equivalent
also long grammatical structures can be substituted
by one word prepositions, conjunctions or verbs.
The main aim of a real-time interlingual subtitler
should be to make sure that subtitles are clear, accurate and accessible
for the user.
Summary
As a summary we would like to stress the following points:
The NER model assesses verbatim accuracy
and the IRA model assesses sensatim accuracy
output text will present alterations based on production techniques
such as omission and compression
and real time subtitlers have to make sure
that subtitles are clear, accurate and accessible for the user
Exercises
The exercises for this video lecture are in the Trainer's Guide
and the PowerPoint file.
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