Combining semantic and syntactic structure for language modeling

Structured language models for speech recognition have been shown to remedy the weaknesses of n-gram models. All current structured language models are, however, limited in that they do not take into account dependencies between non-headwords. We show that non-headword dependencies contribute to significantly improved word error rate, and that a data-oriented parsing model trained on semantically and syntactically annotated data can exploit these dependencies. This paper also contains the first DOP model trained by means of a maximum likelihood reestimation procedure, which solves some of the theoretical shortcomings of previous DOP models.Comment: 4 page

Structured parameter estimation for LFG-DOP using Backoff

Despite its state-of-the-art performance, the Data Oriented Parsing (DOP) model has been shown to suffer from biased parameter estimation, and the good performance seems more the result of ad hoc adjustments than correct probabilistic generalization over the data. In recent work, we developed a new estimation procedure, called Backoff Estimation, for DOP models that are based on Phrase-Structure annotations (so called Tree-DOP models). Backoff Estimation deviates from earlier methods in that it treats the model parameters as a highly structured space of correlated events (backoffs), rather than a set of disjoint events. In this paper we show that the problem of biased estimates also holds for DOP models that are based on Lexical-Functional Grammar annotations (i.e. LFG-DOP), and that the LFG-DOP parameters also constitute a hierarchically structured space. Subsequently, we adapt the Backoff Estimation algorithm from Tree-DOP to LFG-DOP models. Backoff Estimation turns out to be a natural solution to some of the specific problems of robust parsing under LFGDOP

Accuracy-based scoring for DOT: towards direct error minimization for data-oriented translation

In this work we present a novel technique to rescore fragments in the Data-Oriented Translation model based on their contribution to translation accuracy. We describe three new rescoring methods, and present the initial results of a pilot experiment on a small subset of the Europarl corpus. This work is a proof-of-concept, and is the first step in directly optimizing translation decisions solely on the hypothesized accuracy of potential translations resulting from those decisions

Linguistic Constraints in LFG-DOP

LFG-DOP (Bod and Kaplan, 1998, 2003) provides an appealing answer to the question of how probabilistic methods can be incorporated into linguistic theory. However, despite its attractions, the standard model of LFG-DOP suffers from serious problems of overgeneration, because (a) it is unable to define fragments of the right level of generality, and (b) it has no way of capturing the effect of anything except simple positive constraints. We show how the model can be extended to overcome these problems. The question of how probabilistic methods should be incorporated into linguistic theory is important from both a practical, grammar engineering, perspective, and from the perspective of ‘pure ’ linguistic theory. From a practical point of view such techniques are essential if a system is to achieve a useful breadth of coverag

Inducing Tree-Substitution Grammars

Inducing a grammar from text has proven to be a notoriously challenging learning task despite decades of research. The primary reason for its difficulty is that in order to induce plausible grammars, the underlying model must be capable of representing the intricacies of language while also ensuring that it can be readily learned from data. The majority of existing work on grammar induction has favoured model simplicity (and thus learnability) over representational capacity by using context free grammars and first order dependency grammars, which are not sufficiently expressive to model many common linguistic constructions. We propose a novel compromise by inferring a probabilistic tree substitution grammar, a formalism which allows for arbitrarily large tree fragments and thereby better represent complex linguistic structures. To limit the model's complexity we employ a Bayesian non-parametric prior which biases the model towards a sparse grammar with shallow productions. We demonstrate the model's efficacy on supervised phrase-structure parsing, where we induce a latent segmentation of the training treebank, and on unsupervised dependency grammar induction. In both cases the model uncovers interesting latent linguistic structures while producing competitive results. © 2010 Evangelos Theodorou, Jonas Buchli and Stefan Schaal

Incorporating translation quality-oriented features into log-linear models of machine translation

The current state-of-the-art approach to Machine Translation (MT) has limitations which could be alleviated by the use of syntax-based models. Although the benefits of syntax use in MT are becoming clear with the ongoing improvements in string-to-tree and tree-to-string systems, tree-to-tree systems such as Data Oriented Translation (DOT) have, until recently, suffered from lack of training resources, and as a consequence are currently immature, lacking key features compared to Phrase-Based Statistical MT (PB-SMT) systems. In this thesis we propose avenues to bridge the gap between our syntax-based DOT model and state-of-the-art PB-SMT systems. Noting that both types of systems score translations using probabilities not necessarily related to the quality of the translations they produce, we introduce a training mechanism which takes translation quality into account by averaging the edit distance between a translation unit and translation units used in oracle translations. This training mechanism could in principle be adapted to a very broad class of MT systems. In particular, we show how when translating Spanish sentences into English, it leads to improvements in the translation quality of both PB-SMT and DOT. In addition, we show how our method leads to a PB-SMT system which uses significantly less resources and translates significantly faster than the original, while maintaining the improvements in translation quality. We then address the issue of the limited feature set in DOT by defining a new DOT model which is able to exploit features of the complete source sentence. We introduce a feature into this new model which conditions each target word to the source-context it is associated with, and we also make the first attempt at incorporating a language model (LM) to a DOT system. We investigate different estimation methods for our lexical feature (namely Maximum Entropy and improved Kneser-Ney), reporting on their empirical performance. After describing methods which enable us to improve the efficiency of our system, and which allows us to scale to larger training data sizes, we evaluate the performance of our new model on English-to-Spanish translation, obtaining significant translation quality improvements compared to the original DOT system

Resourcing machine translation with parallel treebanks

The benefits of syntax-based approaches to data-driven machine translation (MT) are clear: given the right model, a combination of hierarchical structure, constituent labels and morphological information can be exploited to produce more fluent, grammatical translation output. This has been demonstrated by the recent shift in research focus towards such linguistically motivated approaches. However, one issue facing developers of such models that is not encountered in the development of state-of-the-art string-based statistical MT (SMT) systems is the lack of available syntactically annotated training data for many languages. In this thesis, we propose a solution to the problem of limited resources for syntax-based MT by introducing a novel sub-sentential alignment algorithm for the induction of translational equivalence links between pairs of phrase structure trees. This algorithm, which operates on a language pair-independent basis, allows for the automatic generation of large-scale parallel treebanks which are useful not only for machine translation, but also across a variety of natural language processing tasks. We demonstrate the viability of our automatically generated parallel treebanks by means of a thorough evaluation process during which they are compared to a manually annotated gold standard parallel treebank both intrinsically and in an MT task. Following this, we hypothesise that these parallel treebanks are not only useful in syntax-based MT, but also have the potential to be exploited in other paradigms of MT. To this end, we carry out a large number of experiments across a variety of data sets and language pairs, in which we exploit the information encoded within the parallel treebanks in various components of phrase-based statistical MT systems. We demonstrate that improvements in translation accuracy can be achieved by enhancing SMT phrase tables with linguistically motivated phrase pairs extracted from a parallel treebank, while showing that a number of other features in SMT can also be supplemented with varying degrees of effectiveness. Finally, we examine ways in which synchronous grammars extracted from parallel treebanks can improve the quality of translation output, focussing on real translation examples from a syntax-based MT system