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Lending a Hand: Sign Language Machine Translation

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Lending a Hand: Sign Language Machine Translation. Sara Morrissey NCLT Seminar Series 21 st June 2006. Overview. Introduction What, why, how…? Out with the old… SL Corpora The System …in with the new *new and improved* Lost in Translation Evaluation issues Conclusion.

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slide1

Lending a Hand:

Sign Language Machine Translation

Sara Morrissey

NCLT Seminar Series

21st June 2006

overview
Overview
  • Introduction
    • What, why, how…?
  • Out with the old…
    • SL Corpora
    • The System
  • …in with the new
    • *new and improved*
  • Lost in Translation
    • Evaluation issues
  • Conclusion
slide3

Introduction

  • WHAT ?
  • Sign Language
  • Visually articulated language
  • Linguistic phenomena prevalent to SLs
    • Classifiers
    • Non-manual features (NMFs)
    • Discourse mapping and use of signing space
introduction 2
Introduction (2)
  • WHY ?
  • a)Improve communication

b) Stretching application of EBMT

  • HOW?
  • Our approach
    • Annotated SL corpora
    • Example-based MT employing Marker Hypothesis (Green, 1979)
introduction 3
Introduction (3)
  • Other approaches
    • Transfer - Grieve-Smith, 1999; Marshall & Sáfár, 2002, Sáfár & Marshall 2002; Van Zijl & Barker, 2003
    • Interlingua – Veale et al., 1998; Zhao et al., 2000
    • Multi-path – Huenerfauth, 2004, 2005
    • Statistical – Bauer et al., 1999, Bungeroth & Ney, 2004, 2005, 2006
slide6

Out with the old…Corpora

  • Difficult to find
  • ECHO project
  • Nederlandse Gebarentaal (NGT) corpora
    • 40 minutes of video data
    • 5 Aesop’s fables by two signers and SL poetry
    • Combined corpus of 561 sentences
out with the old annotation
Out with the old… Annotation
  • Why annotate?
    • No formal written form for SLs
    • Linguistic description including NMFs
    • Can include translation making corpus bi/trilingual
    • Time for chunking and aligning present
  • ELAN annotation toolkit
    • Graphical user interface displaying videos and annotations simultaneously (Fig. 1)
    • Time-aligned and non-time-aligned annotations including NMF description, repetition notation and notes on indexing and role.
out with the old the system
Out with the old…The System
  • Segmentation using the ‘Marker Hypothesis’ (MH) (Green, 1979)
    • Analagous to system of (Way & Gough, 2003; Gough & Way, 24a/b)
    • Segments spoken language sentences according to a set of closed class words
    • Chunks start with closed class words and usually encapsulate a concept or an attribute of a concept forming concept chunks, e.g <CONJ> or with tiny curls
out with the old the system 2
Out with the old…The System (2)
  • MH not suitable for use with SL side of corpus due to sparseness of closed class item markers
    • NGT gloss tier segmented based on time spans of its annotations, remaining annotations with same time span grouped with gloss tier segments forming concept chunks similar to English marker chunks
    • Despite different methods, they are successful in forming potentially alignable concept chunks
out with the old the system 3
English chunk

<CONJ> or with tiny curls

NGT chunk

<CHUNK>

(Gloss RH) TINY CURLS

(Gloss LH) TINY CURLS

(Repetition RH) u

(Repetition LH) u

(Eye Gaze) l,d

Out with the old…The System (3)
out with the old the system 4
Out with the old…The System (4)
  • Searches for exact sentence match in aligned bilingual corpus
  • Uses MH to segment input and searches matching or close match chunks in English side of aligned corpus
  • Looks for individual words in the bilingual lexicon
out with the old experiments
Out with the old…Experiments
  • English and Dutch to NGT (Morrissey & Way, 2005)
    • 100 sentences
    • Annotations subjective so evaluation difficult, but promising results
  • NGT to English Dutch
    • Traditional MT evaluation metrics can be applied (SER, WER, PER, BLEU)
    • Sparse output and low scores due to lack of closed class lexical items in NGT
    • Common marker word insertion
slide14

Out with the old…Experiments (2)

  • SER 96% WER 119%
  • PER 78% BLEU 0
  • Example output and reference translation:
    • mouse promised help
    • “you see” said the mouse, “I promised to help you”
slide15

…in with the new

  • New Corpus
    • ~1400 sentences (SunDial and ATIS corpora)
    • Flight information queries
  • ISL signed video version
  • Homespun annotation
    • With view to end product
  • New system
    • OpenLab
lost in translation evaluation issues
Lost in TranslationEvaluation issues
  • Mainstream evaluation techniques
    • Exact text matching
    • No recognition of synonyms, syntactic structure, semantics
    • SLs no gold standard
  • Other possible evaluation metrics
    • Number of content words/number of words in ref translation
    • Evaluation of syntactic or semantic relations
conclusions
Conclusions
  • Basic system
  • Corpus problems - Larger corpus such as ISL one in creation, more scope for matches, annotations subjective
  • EBMT caters for some SL linguistic phenomena
  • Evaluation metrics unsuitable oral <->non-oral translation
future work
Future Work
  • Adding in NMF information
  • Manual analysis
  • Language model to improve output
  • Suitable evaluation metrics
  • Review other writing systems for SLs
  • Avatar…
thank you
Thank You

Questions?

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