We introduce a framework for learning situated Natural Language Interfaces (NLIs) to interactive virtual environments. The framework exploits the non-linguistic context, or situation, explicitly modeled in such interactive applications. This situation model is integrated with a model of word meaning in a principled manner using a noisy channel approach to language understanding. Preliminary experimentation in an independently designed interactive application, i.e. the Mission Rehearsal Exercise (MRE), shows that this situated NLI outperforms a state of the art NLI on both whole frame accuracy and F-Score metrics. Further, use of the situation model in the situated NLI is shown to increase robustness to the noise introduced by the use of automatic speech recognition. Categories and Subject Descriptors

This paper proposes a method to improve word alignment by combining various clues. Our method first trains a baseline statistical IBM word alignment model. Then we improve it with various clues, which are mainly based on features such as lemmatization, translation dictionary, named entities, and chunks. We incorporate these features into an unified framework. Experimental results show that our method improves word alignment quality by achieving a relative error rate reduction of 39.8%. We also conduct phrase-based machine translation based on the word alignment results. Using BLEU as an evaluation metric, our method achieves an absolute improvement of about 0.02 (about 18 % relative) over a baseline method.

We address the modeling, parameter estimation and search challenges that arise from the introduction of reordering models that capture non-local reordering in alignment modeling. In particular, we introduce several reordering models that utilize (pairs of) function words as contexts for alignment reordering. To address the parameter estimation challenge, we propose to estimate these reordering models from a relatively small amount of manuallyaligned corpora. To address the search challenge, we devise an iterative local search algorithm that stochastically explores reordering possibilities. By capturing non-local reordering phenomena, our proposed alignment model bears a closer resemblance to stateof-the-art translation model. Empirical results show significant improvements in alignment quality as well as in translation performance over baselines in a large-scale Chinese-English translation task. 1

Abstract This chapter has two main aims: (i) to present the state-of-the-art in Machine Translation (MT), namely Phrase-Based Statistical MT, together with the major competing paradigms used in MT research and development today; and (ii) to provide an overview of the MT research carried out by my team here at DCU, characterised here in terms of ‘hybrid MT’. In addition, we provide our views on the directions that MT research might take in the near future, and conclude the chapter with lists of further reading for the interested reader.

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The best systems for machine translation of natural language are based on statistical models learned from data. Conventional representation of a statistical translation model requires substantial offline computation and representation in main memory. Therefore, the principal bottlenecks to the amount of data we can exploit and the complexity of models we can use are available memory and CPU time, and current state of the art already pushes these limits. With data size and model complexity continually increasing, a scalable solution to this problem is central to future improvement. Callison-Burch et al. (2005) and Zhang and Vogel (2005) proposed a solution that we call translation by pattern matching, which we bring to fruition in this dissertation. The training data itself serves as a proxy to the model; rules and parameters are computed on demand. It achieves our desiderata of minimal offline computation and compact representation, but is dependent on fast pattern matching algorithms on text. They demonstrated its application to a common model based on the translation of contiguous substrings, but leave some open problems. Among these is a question: can this approach match the performance of conventional methods despite unavoidable differences that it induces in the model? We show how to answer this question affirmatively. The main

We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32 % reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29 % reduction in AER over symmetrized IBM model 4 predictions. 1

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At the end of the 19th century, L. L. Zamenhof proposed Esperanto; it was intended as a global language to be spoken and understood by everyone. The inventor was hoping that a common language could resolve global problems

A word in one language can be translated to zero, one, or several words in other languages. Using word fertility features has been shown to be useful in building word alignment models for statistical machine translation. We built a fertility hidden Markov model by adding fertility to the hidden Markov model. This model not only achieves lower alignment error rate than the hidden Markov model, but also runs faster. It is similar in some ways to IBM Model 4, but is much easier to understand. We use Gibbs sampling for parameter estimation, which is more principled than the neighborhood method used in IBM Model 4. 1