Benchmarking Compositionality with Formal Languages

Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP can acquire this ability while learning from data is an open question. In this paper, we investigate this problem from the perspective of formal languages. We use deterministic finite-state transducers to make an unbounded number of datasets with controllable properties governing compositionality. By randomly sampling over many transducers, we explore which of their properties contribute to learnability of a compositional relation by a neural network. We find that the models either learn the relations completely or not at all. The key is transition coverage, setting a soft learnability limit at 400 examples per transition

HFST runtime format : A compacted transducer format allowing for fast lookup

University of Pretoria,; 978-1-86854-743-2;Peer reviewe

GREAT: open source software for statistical machine translation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-011-9097-6[EN] In this article, the first public release of GREAT as an open-source, statistical machine translation (SMT) software toolkit is described. GREAT is based on a bilingual language modelling approach for SMT, which is so far implemented for n-gram models based on the framework of stochastic finite-state transducers. The use of finite-state models is motivated by their simplicity, their versatility, and the fact that they present a lower computational cost, if compared with other more expressive models. Moreover, if translation is assumed to be a subsequential process, finite-state models are enough for modelling the existing relations between a source and a target language. GREAT includes some characteristics usually present in state-of-the-art SMT, such as phrase-based translation models or a log-linear framework for local features. Experimental results on a well-known corpus such as Europarl are reported in order to validate this software. A competitive translation quality is achieved, yet using both a lower number of model parameters and a lower response time than the widely-used, state-of-the-art SMT system Moses. © 2011 Springer Science+Business Media B.V.Study was supported by the EC (FEDER, FSE), the Spanish government (MICINN, MITyC, “Plan E”, under Grants MIPRCV “Consolider Ingenio 2010”, iTrans2 TIN2009-14511, and erudito.com TSI-020110-2009-439), and the Generalitat Valenciana (Grant Prometeo/2009/014).González Mollá, J.; Casacuberta Nolla, F. (2011). GREAT: open source software for statistical machine translation. Machine Translation. 25(2):145-160. https://doi.org/10.1007/s10590-011-9097-6S145160252Amengual JC, Benedí JM, Casacuberta F, Castaño MA, Castellanos A, Jiménez VM, Llorens D, Marzal A, Pastor M, Prat F, Vidal E, Vilar JM (2000) The EUTRANS-I speech translation system. Mach Transl 15(1-2): 75–103Andrés-Ferrer J, Juan-Císcar A, Casacuberta F (2008) Statistical estimation of rational transducers applied to machine translation. Appl Artif Intell 22(1–2): 4–22Bangalore S, Riccardi G (2002) Stochastic finite-state models for spoken language machine translation. Mach Transl 17(3): 165–184Berstel J (1979) Transductions and context-free languages. B.G. Teubner, Stuttgart, GermanyCasacuberta F, Vidal E (2004) Machine translation with inferred stochastic finite-state transducers. Comput Linguist 30(2): 205–225Casacuberta F, Vidal E (2007) Learning finite-state models for machine translation. Mach Learn 66(1): 69–91Foster G, Kuhn R, Johnson H (2006) Phrasetable smoothing for statistical machine translation. In: Proceedings of the 11th Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA, pp 53–61González J (2009) Aprendizaje de transductores estocásticos de estados finitos y su aplicación en traducción automática. PhD thesis, Universitat Politècnica de València. Advisor: Casacuberta FGonzález J, Casacuberta F (2009) GREAT: a finite-state machine translation toolkit implementing a grammatical inference approach for transducer inference (GIATI). In: Proceedings of the EACL Workshop on Computational Linguistic Aspects of Grammatical Inference, Athens, Greece, pp 24–32Kanthak S, Vilar D, Matusov E, Zens R, Ney H (2005) Novel reordering approaches in phrase-based statistical machine translation. In: Proceedings of the ACL Workshop on Building and Using Parallel Texts: Data-Driven Machine Translation and Beyond, Ann Arbor, MI, pp 167–174Karttunen L (2001) Applications of finite-state transducers in natural language processing. In: Proceedings of the 5th Conference on Implementation and Application of Automata, London, UK, pp 34–46Kneser R, Ney H (1995) Improved backing-off for n-gram language modeling. 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In: Procee- dings of the ACL Workshop on Statistical Machine Translation, Morristown, NJ, pp 135–139Llorens D, Vilar JM, Casacuberta F (2002) Finite state language models smoothed using n-grams. Int J Pattern Recognit Artif Intell 16(3): 275–289Marcu D, Wong W (2002) A phrase-based, joint probability model for statistical machine translation. In: Proceedings of the 7th Conference on Empirical Methods in Natural Language Processing, Morristown, NJ, pp 133–139Mariño JB, Banchs RE, Crego JM, de Gispert A, Lambert P, Fonollosa JAR, Costa-jussà MR (2006) N-gram-based machine translation. Comput Linguist 32(4): 527–549Medvedev YT (1964) On the class of events representable in a finite automaton. In: Moore EF (eds) Sequential machines selected papers. Addison Wesley, Reading, MAMohri M, Pereira F, Riley M (2002) Weighted finite-state transducers in speech recognition. 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A Semi-automatic and Low Cost Approach to Build Scalable Lemma-based Lexical Resources for Arabic Verbs

International audienceThis work presents a method that enables Arabic NLP community to build scalable lexical resources. The proposed method is low cost and efficient in time in addition to its scalability and extendibility. The latter is reflected in the ability for the method to be incremental in both aspects, processing resources and generating lexicons. Using a corpus; firstly, tokens are drawn from the corpus and lemmatized. Secondly, finite state transducers (FSTs) are generated semi-automatically. Finally, FSTsare used to produce all possible inflected verb forms with their full morphological features. Among the algorithm’s strength is its ability to generate transducers having 184 transitions, which is very cumbersome, if manually designed. The second strength is a new inflection scheme of Arabic verbs; this increases the efficiency of FST generation algorithm. The experimentation uses a representative corpus of Modern Standard Arabic. The number of semi-automatically generated transducers is 171. The resulting open lexical resources coverage is high. Our resources cover more than 70% Arabic verbs. The built resources contain 16,855 verb lemmas and 11,080,355 fully, partially and not vocalized verbal inflected forms. All these resources are being made public and currently used as an open package in the Unitex framework available under the LGPL license

DFKI finite-state machine toolkit

Finite-state devices such as finite-state automata and finite-state transducers have been known since the emergence of computer science and are recently extensively used in many areas of language technology. The use of finite-state devices is mainly motivated by their time and space efficiency. In this paper we present the Finite-State Machine Toolkit for building, combining and optimizing the finite-state machines, developed at the Language Technology Lab of the German Research Center for Artificial Intelligence

Inducing Probabilistic Grammars by Bayesian Model Merging

We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are {\em incorporated} by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are {\em merged} to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (`Occam's Razor'). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based $n$-grams, and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13 page

Constrained domain maximum likelihood estimation and the loss function in statistical pattern recognition

In this thesis we present a new estimation algorithm for statistical models which does not incurs in the over-trainning problems. This new estimation techinque, the so-called, constrained domain maximum likelihood estimation (CDMLE) holds all the theoretical properties of the maximum likelihood estimation and furthermore it does not provides overtrained parameter sets. On the other hand, the impliations of the the 0-1 loss function assumption are analysed in the pattern recognition tasks. Specifically, more versatile functions are designed without increasing the optimal classification rule costs. This approach is applied to the statistical machine translation problem.Andrés Ferrer, J. (2008). Constrained domain maximum likelihood estimation and the loss function in statistical pattern recognition. http://hdl.handle.net/10251/13638Archivo delegad