Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

A novel dependency-based evaluation metric for machine translation

Automatic evaluation measures such as BLEU (Papineni et al. (2002)) and NIST (Doddington (2002)) are indispensable in the development of Machine Translation (MT) systems, because they allow MT developers to conduct frequent, fast, and cost-effective evaluations of their evolving translation models. However, most of the automatic evaluation metrics rely on a comparison of word strings, measuring only the surface similarity of the candidate and reference translations, and will penalize any divergence. In effect,a candidate translation expressing the source meaning accurately and fluently will be given a low score if the lexical and syntactic choices it contains, even though perfectly legitimate, are not present in at least one of the references. Necessarily, this score would differ from a much more favourable human judgment that such a translation would receive. This thesis presents a method that automatically evaluates the quality of translation based on the labelled dependency structure of the sentence, rather than on its surface form. Dependencies abstract away from the some of the particulars of the surface string realization and provide a more "normalized" representation of (some) syntactic variants of a given sentence. The translation and reference files are analyzed by a treebank-based, probabilistic Lexical-Functional Grammar (LFG) parser (Cahill et al. (2004)) for English, which produces a set of dependency triples for each input. The translation set is compared to the reference set, and the number of matches is calculated, giving the precision, recall, and f-score for that particular translation. The use of WordNet synonyms and partial matching during the evaluation process allows for adequate treatment of lexical variation, while employing a number of best parses helps neutralize the noise introduced during the parsing stage. The dependency-based method is compared against a number of other popular MT evaluation metrics, including BLEU, NIST, GTM (Turian et al. (2003)), TER (Snover et al. (2006)), and METEOR (Banerjee and Lavie (2005)), in terms of segment- and system-level correlations with human judgments of fluency and adequacy. We also examine whether it shows bias towards statistical MT models. The comparison of the dependency-based method with other evaluation metrics is then extended to languages other than English: French, German, Spanish, and Japanese, where we apply our method to dependencies generated by Microsoft's NLPWin analyzer (Corston-Oliver and Dolan (1999); Heidorn (2000)) as well as, in the case of the Spanish data, those produced by the treebank-based, probabilistic LFG parser of Chrupa la and van Genabith (2006a,b)

Learning from Noisy Data in Statistical Machine Translation

In dieser Arbeit wurden Methoden entwickelt, die in der Lage sind die negativen Effekte von verrauschten Daten in SMT Systemen zu senken und dadurch die Leistung des Systems zu steigern. Hierbei wird das Problem in zwei verschiedenen Schritten des Lernprozesses behandelt: Bei der Vorverarbeitung und während der Modellierung. Bei der Vorverarbeitung werden zwei Methoden zur Verbesserung der statistischen Modelle durch die Erhöhung der Qualität von Trainingsdaten entwickelt. Bei der Modellierung werden verschiedene Möglichkeiten vorgestellt, um Daten nach ihrer Nützlichkeit zu gewichten. Zunächst wird der Effekt des Entfernens von False-Positives vom Parallel Corpus gezeigt. Ein Parallel Corpus besteht aus einem Text in zwei Sprachen, wobei jeder Satz einer Sprache mit dem entsprechenden Satz der anderen Sprache gepaart ist. Hierbei wird vorausgesetzt, dass die Anzahl der Sätzen in beiden Sprachversionen gleich ist. False-Positives in diesem Sinne sind Satzpaare, die im Parallel Corpus gepaart sind aber keine Übersetzung voneinander sind. Um diese zu erkennen wird ein kleiner und fehlerfreier paralleler Corpus (Clean Corpus) vorausgesetzt. Mit Hilfe verschiedenen lexikalischen Eigenschaften werden zuverlässig False-Positives vor der Modellierungsphase gefiltert. Eine wichtige lexikalische Eigenschaft hierbei ist das vom Clean Corpus erzeugte bilinguale Lexikon. In der Extraktion dieses bilingualen Lexikons werden verschiedene Heuristiken implementiert, die zu einer verbesserten Leistung führen. Danach betrachten wir das Problem vom Extrahieren der nützlichsten Teile der Trainingsdaten. Dabei ordnen wir die Daten basierend auf ihren Bezug zur Zieldomaine. Dies geschieht unter der Annahme der Existenz eines guten repräsentativen Tuning Datensatzes. Da solche Tuning Daten typischerweise beschränkte Größe haben, werden Wortähnlichkeiten benutzt um die Abdeckung der Tuning Daten zu erweitern. Die im vorherigen Schritt verwendeten Wortähnlichkeiten sind entscheidend für die Qualität des Verfahrens. Aus diesem Grund werden in der Arbeit verschiedene automatische Methoden zur Ermittlung von solche Wortähnlichkeiten ausgehend von monoligual und biligual Corpora vorgestellt. Interessanterweise ist dies auch bei beschränkten Daten möglich, indem auch monolinguale Daten, die in großen Mengen zur Verfügung stehen, zur Ermittlung der Wortähnlichkeit herangezogen werden. Bei bilingualen Daten, die häufig nur in beschränkter Größe zur Verfügung stehen, können auch weitere Sprachpaare herangezogen werden, die mindestens eine Sprache mit dem vorgegebenen Sprachpaar teilen. Im Modellierungsschritt behandeln wir das Problem mit verrauschten Daten, indem die Trainingsdaten anhand der Güte des Corpus gewichtet werden. Wir benutzen Statistik signifikante Messgrößen, um die weniger verlässlichen Sequenzen zu finden und ihre Gewichtung zu reduzieren. Ähnlich zu den vorherigen Ansätzen, werden Wortähnlichkeiten benutzt um das Problem bei begrenzten Daten zu behandeln. Ein weiteres Problem tritt allerdings auf sobald die absolute Häufigkeiten mit den gewichteten Häufigkeiten ersetzt werden. In dieser Arbeit werden hierfür Techniken zur Glättung der Wahrscheinlichkeiten in dieser Situation entwickelt. Die Größe der Trainingsdaten werden problematisch sobald man mit Corpora von erheblichem Volumen arbeitet. Hierbei treten zwei Hauptschwierigkeiten auf: Die Länge der Trainingszeit und der begrenzte Arbeitsspeicher. Für das Problem der Trainingszeit wird ein Algorithmus entwickelt, der die rechenaufwendigen Berechnungen auf mehrere Prozessoren mit gemeinsamem Speicher ausführt. Für das Speicherproblem werden speziale Datenstrukturen und Algorithmen für externe Speicher benutzt. Dies erlaubt ein effizientes Training von extrem großen Modellne in Hardware mit begrenztem Speicher

Domain adaptation for statistical machine translation of corporate and user-generated content

The growing popularity of Statistical Machine Translation (SMT) techniques in recent years has led to the development of multiple domain-specic resources and adaptation scenarios. In this thesis we address two important and industrially relevant adaptation scenarios, each suited to different kinds of content. Initially focussing on professionally edited `enterprise-quality' corporate content, we address a specic scenario of data translation from a mixture of different domains where, for each of them domain-specific data is available. We utilise an automatic classifier to combine multiple domain-specific models and empirically show that such a configuration results in better translation quality compared to both traditional and state-of-the-art techniques for handling mixed domain translation. In the second phase of our research we shift our focus to the translation of possibly `noisy' user-generated content in web-forums created around products and services of a multinational company. Using professionally edited translation memory (TM) data for training, we use different normalisation and data selection techniques to adapt SMT models to noisy forum content. In this scenario, we also study the effect of mixture adaptation using a combination of in-domain and out-of-domain data at different component levels of an SMT system. Finally we focus on the task of optimal supplementary training data selection from out-of-domain corpora using a novel incremental model merging mechanism to adapt TM-based models to improve forum-content translation quality

Proceedings of the 17th Annual Conference of the European Association for Machine Translation

Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT

Paraphrasing and Translation

Paraphrasing and translation have previously been treated as unconnected natural lan¬ guage processing tasks. Whereas translation represents the preservation of meaning when an idea is rendered in the words in a different language, paraphrasing represents the preservation of meaning when an idea is expressed using different words in the same language. We show that the two are intimately related. The major contributions of this thesis are as follows:• We define a novel technique for automatically generating paraphrases using bilingual parallel corpora, which are more commonly used as training data for statistical models of translation.• We show that paraphrases can be used to improve the quality of statistical ma¬ chine translation by addressing the problem of coverage and introducing a degree of generalization into the models.• We explore the topic of automatic evaluation of translation quality, and show that the current standard evaluation methodology cannot be guaranteed to correlate with human judgments of translation quality.Whereas previous data-driven approaches to paraphrasing were dependent upon either data sources which were uncommon such as multiple translation of the same source text, or language specific resources such as parsers, our approach is able to harness more widely parallel corpora and can be applied to any language which has a parallel corpus. The technique was evaluated by replacing phrases with their para¬ phrases, and asking judges whether the meaning of the original phrase was retained and whether the resulting sentence remained grammatical. Paraphrases extracted from a parallel corpus with manual alignments are judged to be accurate (both meaningful and grammatical) 75% of the time, retaining the meaning of the original phrase 85% of the time. Using automatic alignments, meaning can be retained at a rate of 70%.Being a language independent and probabilistic approach allows our method to be easily integrated into statistical machine translation. A paraphrase model derived from parallel corpora other than the one used to train the translation model can be used to increase the coverage of statistical machine translation by adding translations of previously unseen words and phrases. If the translation of a word was not learned, but a translation of a synonymous word has been learned, then the word is paraphrased and its paraphrase is translated. Phrases can be treated similarly. Results show that augmenting a state-of-the-art SMT system with paraphrases in this way leads to significantly improved coverage and translation quality. For a training corpus with 10,000 sentence pairs, we increase the coverage of unique test set unigrams from 48% to 90%, with more than half of the newly covered items accurately translated, as opposed to none in current approaches

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Discourse-level features for statistical machine translation

Machine Translation (MT) has progressed tremendously in the past two decades. The rule-based and interlingua approaches have been superseded by statistical models, which learn the most likely translations from large parallel corpora. System design does not amount anymore to crafting syntactical transfer rules, nor does it rely on a semantic representation of the text. Instead, a statistical MT system learns the most likely correspondences and re-ordering of chunks of source words and target words from parallel corpora that have been word-aligned. With this procedure and millions of parallel source and target language sentences, systems can generate translations that are intelligible and require minimal post-editing efforts from the human user. Nevertheless, it has been recognized that the statistical MT paradigm may fall short of modeling a number of linguistic phenomena that are established beyond the phrase level. Research in statistical MT has addressed discourse phenomena explicitly only in the past four years. When it comes to textual coherence structure, cohesive ties relate sentences and entire paragraphs argumentatively to each other. This text structure has to be rendered appropriately in the target text so that it conveys the same meaning as the source text. The lexical and syntactical means through which these cohesive markers are expressed may diverge considerably between languages. Frequently, these markers include discourse connectives, which are function words such as however, instead, since, while, which relate spans of text to each other, e.g. for temporal ordering, contrast or causality. Moreover, to establish the same temporal ordering of events described in a text, the conjugation of verbs has to be coherently translated. The present thesis proposes methods for integrating discourse features into statistical MT. We pre-process the source text prior to automatic translation, focusing on two specific discourse phenomena: discourse connectives and verb tenses. Hand-crafted rules are not required in our proposal; instead, machine learning classifiers are implemented that learn to recognize discourse relations and predict translations of verb tenses. Firstly, we have designed new sets of semantically-oriented features and classifiers to advance the state of the art in automatic disambiguation of discourse connectives. We hereby profited from our multilingual setting and incorporated features that are based on MT and on the insights we gained from contrastive linguistic analysis of parallel corpora. In their best configurations, our classifiers reach high performances (0.7 to 1.0 F1 score) and can therefore reliably be used to automatically annotate the large corpora needed to train SMT systems. Issues of manual annotation and evaluation are discussed as well, and solutions are provided within new annotation and evaluation procedures. As a second contribution, we implemented entire SMT systems that can make use of the (automatically) annotated discourse information. Overall, the thesis confirms that these techniques are a practical solution that leads to global improvements in translation in ranges of 0.2 to 0.5 BLEU score. Further evaluation reveals that in terms of connectives and verb tenses, our statistical MT systems improve the translation of these phenomena in ranges of up to 25%, depending on the performance of the automatic classifiers and on the data sets used