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Crowdsourcing Statistical Machine Translation for Efficient Text Normalization

This paper discusses a method for generating text normalization systems through crowdsourcing, leveraging statistical machine translation (SMT). By using internet users to annotate and normalize text, we create SMT models that translate original text into normalized forms. The approach aims to analyze efficiency across different languages, reduce user effort through iterative text normalization, and provide a web-based interface for simple user interaction. Results show competitive performance of SMT models compared to native speaker norms, with potential applications in high-quality text normalization across various languages.

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Crowdsourcing Statistical Machine Translation for Efficient Text Normalization

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  1. Statistical Machine Translation based Text Normalization with Crowdsourcing Tim Schlippe, Chenfei Zhu, Daniel Lemcke, Tanja Schultz tim.schlippe@kit.edu • 1. Overview • Introduction • Text normalizationsystemgenerationcanbe time-comsuming • Constructionwiththesupportof Internet users (crowdsourcing): • 1. Based on textnormalizedbyusersand original text, statisticalmachine • translation (SMT) modelsarecreated (Schlippe et al., 2010) • 2. These SMT modelsareappliedto"translate" original into normalized text • Everybodywhocanspeakandwritethetargetlanguagecanbuildthetextnormalizationsystem due to the simple self-explanatory user interface and the automatic generation of the SMT models • Annotation of training data can be performed in parallel by many usersGoals ofthispaper • Analyzeefficiencyfor different languages • Embed English annotation process for training data in MTurk • Reduceuser effort by iterative text normalization generation and application • 2. Experimental Setup • Pre-Normalization • LI-rulebyour Rapid Language Adaptation Toolkit (RLAT) • Language-specificnormalizationby Internet users • User is provided with a simple readme file that explains how to normalize the sentences • Web-based user interface for text normalization • Keep theeffortfortheuserslow: • Sentencestonormalizeare displayed twice: The upper line shows the non-normalized sentence, the lower line is editable • Evaluation • Compare quality (Edit distance) of output sentences (1k for DE and EN, 500 for BG) derived from SMT, LI-rule , LS-rule and hybrid to quality of text normalized by native speakers • Create 3-gram LMs from hypotheses (1k for DE and EN, 500 for BG) and compare their perplexities (PPLs) on manually normalizedtestsentences (500 for DE and EN, 100 for BG) , Web-based user interface for text normalization Language-independent and -specific text normalization Performance overamountoftrainingdata • 4. Conclusionand Future Work • A crowdsourcingapproachfor SMT-basedlanguage-specifictext normalization: Native speakers deliver resources to build normalization systems by editing text in our web interface • Results of SMT which were close to LS-rule for French, even outperformed LS-rule for Bulgarian, English and German, hybrid better, close to human • Annotation process for English training data could be realized fast and at low cost with MTurk, however need for methods to detect and rejectTurkers’ spam • Reduction of editing effort in the annotation process for training data with iterative-SMT and iterative-hybrid 3. Experiments andResults Edit Distancereductionwith iterative SMT / hybrid ICASSP 2013 – The 38th International Conference on Acoustics, Speech, and Signal Processing

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