Disambiguation strategies for data-oriented translation

The Data-Oriented Translation (DOT) model { originally proposed in (Poutsma, 1998, 2003) and based on Data-Oriented Parsing (DOP) (e.g. (Bod, Scha, & Sima'an, 2003)) { is best described as a hybrid model of translation as it combines examples, linguistic information and a statistical translation model. Although theoretically interesting, it inherits the computational complexity associated with DOP. In this paper, we focus on one computational challenge for this model: efficiently selecting the `best' translation to output. We present four different disambiguation strategies in terms of how they are implemented in our DOT system, along with experiments which investigate how they compare in terms of accuracy and efficiency

Tree edit distance as a baseline approach for paraphrase representation

Finding an adequate paraphrase representation formalism is a challenging issue in Natural Language Processing. In this paper, we analyse the performance of Tree Edit Distance as a paraphrase representation baseline. Our experiments using Edit Distance Textual Entailment Suite show that, as Tree Edit Distance consists of a purely syntactic approach, paraphrase alternations not based on structural reorganizations do not find an adequate representation. They also show that there is much scope for better modelling of the way trees are aligned

Methods for taking semantic graphs apart and putting them back together again

The thesis develops a competitive compositional semantic parser for Abstract Meaning Representation (AMR). This approach combines a neural model with mechanisms that echo ideas from compositional semantic construction in a new, simple dependency structure. The thesis first tackles the task of generating structured training data necessary for a compositional approach, by developing the linguistically motivated AM algebra. Encoding the terms over the AM algebra as dependency trees yields a simple semantic parsing model where neural tagging and dependency models predict interpretable, meaningful operations that construct the AMR.Diese Dissertation entwickelt einen kompositionellen semantischen Parser für den Graphformalismus Abstract Meaning Representation (AMR). Der Ansatz kombiniert ein neuronales Modell mit Mechanismen, die Ideen der klassischen kompositionellen semantischen Konstruktion widerspiegeln. Die Arbeit geht zunächst das Problem an, strukturierte latente Trainingsdaten zu erzeugen die für den kompositionellen Ansatz nötig sind. Für diesen Zweck wird die linguistisch motivierte AM Algebra entwickelt. Indem die Terme der AM Algebra als Dependenzbäume ausgedrückt werden, erhalten wir ein Modell für semantisches Parsen, in dem neuronale Tagging- und Dependenzmodelle interpretierbare, aussagekräftige Operationen vorhersagen die dann den AMR Graphen erzeugen. Damit erreicht das Modell starke Evaluationsergebnisse und deutliche Verbesserungen gegenüber einem weniger strukturierten Vergleichsmodell.DF

Nlp Challenges for Machine Translation from English to Indian Languages

This Natural Langauge processing is carried particularly on English-Kannada/Telugu. Kannada is a language of India. The Kannada language has a classification of Dravidian, Southern, Tamil-Kannada, and Kannada. Regions Spoken: Kannada is also spoken in Karnataka, Andhra Pradesh, Tamil Nadu, and Maharashtra. Population: The total population of people who speak Kannada is 35,346,000, as of 1997. Alternate Name: Other names for Kannada are Kanarese, Canarese, Banglori, and Madrassi. Dialects: Some dialects of Kannada are Bijapur, Jeinu Kuruba, and Aine Kuruba. There are about 20 dialects and Badaga may be one. Kannada is the state language of Karnataka. About 9,000,000 people speak Kannada as a second language. The literacy rate for people who speak Kannada as a first language is about 60%, which is the same for those who speak Kannada as a second language (in India). Kannada was used in the Bible from 1831-2000. Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translatio

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

A Formal Model of Ambiguity and its Applications in Machine Translation

Systems that process natural language must cope with and resolve ambiguity. In this dissertation, a model of language processing is advocated in which multiple inputs and multiple analyses of inputs are considered concurrently and a single analysis is only a last resort. Compared to conventional models, this approach can be understood as replacing single-element inputs and outputs with weighted sets of inputs and outputs. Although processing components must deal with sets (rather than individual elements), constraints are imposed on the elements of these sets, and the representations from existing models may be reused. However, to deal efficiently with large (or infinite) sets, compact representations of sets that share structure between elements, such as weighted finite-state transducers and synchronous context-free grammars, are necessary. These representations and algorithms for manipulating them are discussed in depth in depth. To establish the effectiveness and tractability of the proposed processing model, it is applied to several problems in machine translation. Starting with spoken language translation, it is shown that translating a set of transcription hypotheses yields better translations compared to a baseline in which a single (1-best) transcription hypothesis is selected and then translated, independent of the translation model formalism used. More subtle forms of ambiguity that arise even in text-only translation (such as decisions conventionally made during system development about how to preprocess text) are then discussed, and it is shown that the ambiguity-preserving paradigm can be employed in these cases as well, again leading to improved translation quality. A model for supervised learning that learns from training data where sets (rather than single elements) of correct labels are provided for each training instance and use it to learn a model of compound word segmentation is also introduced, which is used as a preprocessing step in machine translation