Predicting and Using a Pragmatic Component of Lexical Aspect of Simple Past Verbal Tenses for Improving E nglish-to- F rench Machine Translation (2016)

Abstract: This paper proposes a method for improving the results of a statistical Machine Translation system using boundedness, a pragmatic component of the verbal phrase’s lexical aspect. First, the paper presents manual and automatic annotation experiments for lexical aspect in EnglishFrench parallel corpora. It will be shown that this aspectual property is identified and classified with ease both by humans and by automatic systems. Second, Statistical Machine Translation experiments using the boundedness annotations are presented. These experiments show that the information regarding lexical aspect is useful to improve the output of a Machine Translation system in terms of better choices of verbal tenses in the target language, as well as better lexical choices. Ultimately, this work aims at providing a method for the automatic annotation of data with boundedness information and at contributing to Machine Translation by taking into account linguistic data.

## introduction this paper aims at improving the results of statistical machine translation (smt) systems with respect to verbal tenses. verbal tenses are the primary linguistic source of temporal reference, i.e. the localization of events and states in time. verbal tenses express temporal location of eventualities foot_0 with respect to the moment of speech and with respect to each other. for example, the past perfect form in sentence (1) locates the two eventualities before s ('moment of speech'), hence in the past, and the eventuality of hard working before the moment when another eventuality, the son's disappearance, occurs. the choice of the verbal tense depends on fine-grained temporal interpretations of the utterance in context. in this paper, we show the translation of verbal tenses can be improved at the sentence-level when we make use of a pragmatic component of the verbal phrase's lexical aspect, i.e. boundedness, and the way in which it is perceived by human speakers. we have investigated the usefulness of boundedness for correctly translating the english simple past (sp) into french using a smt system. the four most frequently used translations of the sp in french are: passé composé (pc), imparfait (imp), passé simple (ps) and présent (pres). @xcite showed that this mapping has a skewed distribution in favour of the pc translation. since smt systems favour the most frequent translation and make little use of context or meaning, the other three possible translations are often not generated. this yields translations that can be in some cases ungrammatical and in other cases grammatical but not native-like. as an illustration, in figure 1 we show a sentence translated into french by a baseline system built for our experiments (section 5) together with its translation by google translate foot_1 . in the example, the verb in bold is an english sp verb translated using the french pc by both systems. however, the reference translation proposes a pres form. our hypothesis is that boundedness is relevant to disambiguate french translations of the english sp. with this assumption, the main goal of this paper is to improve the mt of english sp verbs by integrating this temporal property into a smt system. ## human annotation the corpus data used in our work was provided by @xcite . it is the same corpus as that used in @xcite 's study. @xcite built a parallel corpus, consisting of texts originally written in english and their translation into french. these texts were randomly selected and belong to the following stylistic registers: literature, journalism, discussions of the european union parliament (the europarl corpus) and european union legislation (the jrc-acquis corpus). @xcite found that english sp was not translated by a single or specific fr tense. in order to investigate this translation divergence, grisot and cartoni built a smaller sub-corpus with 435 english sentences containing sp tokens and their corresponding french translation. this corpus was manually-annotated with linguistic (semantic and pragmatic) properties, as suggested in @xcite : narrativity, perfectivity and boundedness. the human annotation for each of these features was independent of the others. for each feature, the judges had access only to the english data containing sp occurrences and to the annotation guidelines. the annotated data was analyzed in its totality after the various human annotation were done @xcite . @xcite made use of this corpus annotated with narrativity in their mt experiments. here, we use the same corpus and focus on boundedness and its utility for determining the verbal tense used in a target language. in what follows, we will first describe the human-annotation experiments with boundedness. ## predicting the boundedness of english sp verbs investigating the pragmatics of temporal reference, @xcite proposed that it may be expressed through several linguistic expression august 2016 sentence verb grammatical narrativity bounded-fr infinitive aspect ness tense in one instance, kazakhstan revealed the existence of a ton of highly enriched uranium and asked the united states to remove it, lest it fall into the wrong hands. asked perfective narrative bounded pc to ask he fascinated everybody who was worth fascinating and a great number of people who were not. he was often wilful and petulant, and i used to think him dreadfully insincere. fascinated imperfective non-narrative unbounded imp to fascinate a few days ago, in a manner of speaking, we said that bin laden had provided the impetus for implementing methods for fighting terrorism that the commission had been planning and that parliament had requested some time ago. said perfective narrative bounded imp to say although the us viewed musharraf as an agent of change, he has never achieved domestic political legitimacy, and his policies were seen as rife with contradictions. were seen imperfective non-narrative unbounded pc to see figure 4 example of human annotated corpus data markers, such as verbal tenses (expressing both tense and grammatical aspect), lexical aspect, temporal connectives and adverbials, as well as non-linguistic types of information, such as contextual and world knowledge. linguistic markers are correlated and the value of a marker can be predicted based on the values of the other markers. the leading hypothesis in the experiments presented below is that the linguistic information available in the sentence where the verb occurs can be used to predict the boundedness status of english sp verbs due to its context-dependent character. in this section, our motivation is threefold. our foremost goal is to predict the the boundedness value of english sp verbs from the corpus previously described. the second goal is to understand better the role of each of the linguistic factors in predicting this pragmatic component of the lexical aspect of sp verbs, by isolating each of them. our third and final goal is to propose a classifier trained on automatically generated features only. since human annotated data is expensive and time consuming, training a classifier for the task would expedite the annotation of large amounts of text. ## using the predictions for machine translation in this final part, we assess how much a smt system enhanced with boundedness knowledge improves the translation of the english sp into french. phrase-based smt systems often generate only the most frequent translation possibility, the pc, as the default. our goal is to provide a system with bounded/unbounded labels in order to boost the other three tenses, improving the tense translation choice. given that there is no sufficiently large data set annotated with aspect information to train a smt system, we annotate our own using the classifier trained before, in experiment 2 (section 4). the data is taken from the multiun corpus, a corpus of translated documents from the united nations, provided by @xcite . all english sp verbs are identified and labeled as either bounded or unbounded automatically. ## conclusions in this paper, we proposed a method to disambiguate and improve the smt of english sp verbs into french. this single english tense has four possible translations in french. the method combines knowledge about a pragmatic componenent of aspect which was operationalized as the boundedness factor in a smt system. this method proved to have good results with respect to the targeted verbal tenses without decreasing the quality of the translation of the surrounding words in the sentence. indeed, manual evaluation of the translated texts showed that correctly labeled verbs with boundedness presented a better tense translation. with this work, we hope to have contributed building a more natural and coherent mt output in terms of adecuacy and fluency 9 , which are august 2016 two defining desirable criteria for machine translation output. additionally, we built two classifiers for boundedness, one including a larger set of features including oracle features and the other one trained on automatic features only. the first showed that boundedness can be annotated reliably and set the upper-bound performance of the classification task. the second allowed us to label a large corpus based on a minimal and affordable quantity of manually annotated data. regarding the classification tasks, we found that training on such a small corpus produced very good results. compared to other latent features difficult for automatic prediction such as narrativity or aspect markers in chinese, the component of aspect that we examined seems more feasible to learn. we obtained results around 15% better than those for narrativity prediction @xcite for instance. @xcite points out that according to a mixed statistical model estimated on the manually annotated corpus of 435 sentences, french tense is significantly determined by the interaction between narrativity and boundedness. such theoretical insight is unfortunately hard to test empirically and will be investigated in further work. using the same method presented in this paper, we can make two suggestions for using the information about the interaction between narrativity and boundedness. a classifier could be built to predict the narrativity and boundedness at the same time, i.e. a four classes task (+narrative+bounded, +narrative-unbounded, -narrative+bounded, -narrative+unbounded). the factored model would thereafter have one factor. another solution would be to train two classifiers, one for narrativity and another for boundedness. this would produce two pairs of independent labels and hence two different factors in the factored model. it should be tested whether diluting the information in such a way would still add knowledge to the system, since the distributions may become scarce.