A new method for learning Phrase Based Machine Translation with Multivariate Mutual Information

International audienceCurrent statistical machine translation systems usually build an initial word-to-word alignments before learning phrase translation pairs. This operation needs so many matching between di erent single words of both considered languages. We propose a new approach for phrase-based machine translation which does not need any word alignments, it is based on inter-lingual triggers determined by Multivariate Mutual Information. This algorithm segments sentences into phrases and nds their alignments simultaneously. The main objective is to build directly valid alignments between source and target phrases. Inspite of the youth of this method, experiments showed that the results are competitive but needs some more e orts in order to overcome the one of state-of-the-art methods

Character-level and syntax-level models for low-resource and multilingual natural language processing

There are more than 7000 languages in the world, but only a small portion of them benefit from Natural Language Processing resources and models. Although languages generally present different characteristics, “cross-lingual bridges” can be exploited, such as transliteration signals and word alignment links. Such information, together with the availability of multiparallel corpora and the urge to overcome language barriers, motivates us to build models that represent more of the world’s languages. This thesis investigates cross-lingual links for improving the processing of low-resource languages with language-agnostic models at the character and syntax level. Specifically, we propose to (i) use orthographic similarities and transliteration between Named Entities and rare words in different languages to improve the construction of Bilingual Word Embeddings (BWEs) and named entity resources, and (ii) exploit multiparallel corpora for projecting labels from high- to low-resource languages, thereby gaining access to weakly supervised processing methods for the latter. In the first publication, we describe our approach for improving the translation of rare words and named entities for the Bilingual Dictionary Induction (BDI) task, using orthography and transliteration information. In our second work, we tackle BDI by enriching BWEs with orthography embeddings and a number of other features, using our classification-based system to overcome script differences among languages. The third publication describes cheap cross-lingual signals that should be considered when building mapping approaches for BWEs since they are simple to extract, effective for bootstrapping the mapping of BWEs, and overcome the failure of unsupervised methods. The fourth paper shows our approach for extracting a named entity resource for 1340 languages, including very low-resource languages from all major areas of linguistic diversity. We exploit parallel corpus statistics and transliteration models and obtain improved performance over prior work. Lastly, the fifth work models annotation projection as a graph-based label propagation problem for the part of speech tagging task. Part of speech models trained on our labeled sets outperform prior work for low-resource languages like Bambara (an African language spoken in Mali), Erzya (a Uralic language spoken in Russia’s Republic of Mordovia), Manx (the Celtic language of the Isle of Man), and Yoruba (a Niger-Congo language spoken in Nigeria and surrounding countries)

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Bridging the gap between textual and formal business process representations

Tesi en modalitat de compendi de publicacionsIn the era of digital transformation, an increasing number of organizations are start ing to think in terms of business processes. Processes are at the very heart of each business, and must be understood and carried out by a wide range of actors, from both technical and non-technical backgrounds alike. When embracing digital transformation practices, there is a need for all involved parties to be aware of the underlying business processes in an organization. However, the representational complexity and biases of the state-of-the-art modeling notations pose a challenge in understandability. On the other hand, plain language representations, accessible by nature and easily understood by everyone, are often frowned upon by technical specialists due to their ambiguity. The aim of this thesis is precisely to bridge this gap: Between the world of the techni cal, formal languages and the world of simpler, accessible natural languages. Structured as an article compendium, in this thesis we present four main contributions to address specific problems in the intersection between the fields of natural language processing and business process management.A l’era de la transformació digital, cada vegada més organitzacions comencen a pensar en termes de processos de negoci. Els processos són el nucli principal de tota empresa i, com a tals, han de ser fàcilment comprensibles per un ampli ventall de rols, tant perfils tècnics com no-tècnics. Quan s’adopta la transformació digital, és necessari que totes les parts involucrades estiguin ben informades sobre els protocols implantats com a part del procés de digitalització. Tot i això, la complexitat i biaixos de representació dels llenguatges de modelització que actualment conformen l’estat de l’art sovint en dificulten la seva com prensió. D’altra banda, les representacions basades en documentació usant llenguatge natural, accessibles per naturalesa i fàcilment comprensibles per tothom, moltes vegades són vistes com un problema pels perfils més tècnics a causa de la presència d’ambigüitats en els textos. L’objectiu d’aquesta tesi és precisament el de superar aquesta distància: La distància entre el món dels llenguatges tècnics i formals amb el dels llenguatges naturals, més accessibles i senzills. Amb una estructura de compendi d’articles, en aquesta tesi presentem quatre grans línies de recerca per adreçar problemes específics en aquesta intersecció entre les tecnologies d’anàlisi de llenguatge natural i la gestió dels processos de negoci.Postprint (published version

A new method for learning Phrase Based Machine Translation with Multivariate Mutual Information

International audienceCurrent statistical machine translation systems usually build an initial word-to-word alignments before learning phrase translation pairs. This operation needs so many matching between di erent single words of both considered languages. We propose a new approach for phrase-based machine translation which does not need any word alignments, it is based on inter-lingual triggers determined by Multivariate Mutual Information. This algorithm segments sentences into phrases and nds their alignments simultaneously. The main objective is to build directly valid alignments between source and target phrases. Inspite of the youth of this method, experiments showed that the results are competitive but needs some more e orts in order to overcome the one of state-of-the-art methods

Genuine phrase-based statistical machine translation with supervision

This thesis addresses mainly two issues that have not been addressed in Statis-tical Machine Translation. One issue is that even though research has been evolving from word-based approaches to phrase-based ones, because words were consistently found to be inappropriate translation units, the fact is that words are still considered in the composition of phrases, either to determine translation equivalents or to check language fluency. Such consideration might result in the attempt of establishing relations between words within a phrase translation equivalent even when sometimes its phrases should be considered as a whole. Attempts to further partition such phrases would produce incorrect translation units that would introduce unwanted noise in the translation pro-cess. Besides, the internal fluency of an identified multi-word phrase should not require checking. As such, phrases should indeed be considered units, avoiding incorrect translation equivalents that might be identified from their partition, as well as only considering the fluency of a phrase with other phrases and not within the phrase itself. The other issue is that supervision, in the form of trans-lation lexica, is generally overlooked, with SMT research focusing mainly on the identification of translation units without any human intervention and without considering already known translation units. As such, no importance has been attributed to the inclusion of verified lexica, with only some rarely used dic-tionaries to score translation candidates and not really as a source of translation units. Indeed, translation equivalents should be memorized, checked and used as a source of translation units, avoiding the need to keep identifying the same translation units, in particular if those are frequently used. This Thesis presents a truly Phrase-Based approach to SMT, using contiguous and non-contiguous phrases, along with Supervision, in which phrases are not divided and verified lexica is built, kept and used to propose translations of complete sentences

Multiword expression processing: A survey

Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives