A syntactified direct translation model with linear-time decoding

Recent syntactic extensions of statistical translation models work with a synchronous context-free or tree-substitution grammar extracted from an automatically parsed parallel corpus. The decoders accompanying these extensions typically exceed quadratic time complexity. This paper extends the Direct Translation Model 2 (DTM2) with syntax while maintaining linear-time decoding. We employ a linear-time parsing algorithm based on an eager, incremental interpretation of Combinatory Categorial Grammar (CCG). As every input word is processed, the local parsing decisions resolve ambiguity eagerly, by selecting a single supertag–operator pair for extending the dependency parse incrementally. Alongside translation features extracted from the derived parse tree, we explore syntactic features extracted from the incremental derivation process. Our empirical experiments show that our model significantly outperforms the state-of-the art DTM2 system

Naturalizing a Programming Language via Interactive Learning

Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language. To bridge this gap, we start with a core programming language and allow users to "naturalize" the core language incrementally by defining alternative, more natural syntax and increasingly complex concepts in terms of compositions of simpler ones. In a voxel world, we show that a community of users can simultaneously teach a common system a diverse language and use it to build hundreds of complex voxel structures. Over the course of three days, these users went from using only the core language to using the naturalized language in 85.9\% of the last 10K utterances.Comment: 10 pages, ACL201

Source sentence simplification for statistical machine translation

Long sentences with complex syntax and long-distance dependencies pose difficulties for machine translation systems. Short sentences, on the other hand, are usually easier to translate. We study the potential of addressing this mismatch using text simplifi- cation: given a simplified version of the full input sentence, can we use it in addition to the full input to improve translation? We show that the spaces of original and simplified translations can be effectively combined using translation lattices and compare two decoding approaches to process both inputs at different levels of integration. We demonstrate on source-annotated portions of WMT test sets and on top of strong baseline systems combining hierarchical and neural translation for two language pairs that source simplification can help to improve translation quality.This work was supported by the EPSRC grant Improving Target Language Fluency in Statistical Machine Translation, grant number EP/L027623/1

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

Good Features to Correlate for Visual Tracking

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin

Translation-based Ranking in Cross-Language Information Retrieval

Today's amount of user-generated, multilingual textual data generates the necessity for information processing systems, where cross-linguality, i.e the ability to work on more than one language, is fully integrated into the underlying models. In the particular context of Information Retrieval (IR), this amounts to rank and retrieve relevant documents from a large repository in language A, given a user's information need expressed in a query in language B. This kind of application is commonly termed a Cross-Language Information Retrieval (CLIR) system. Such CLIR systems typically involve a translation component of varying complexity, which is responsible for translating the user input into the document language. Using query translations from modern, phrase-based Statistical Machine Translation (SMT) systems, and subsequently retrieving monolingually is thus a straightforward choice. However, the amount of work committed to integrate such SMT models into CLIR, or even jointly model translation and retrieval, is rather small. In this thesis, I focus on the shared aspect of ranking in translation-based CLIR: Both, translation and retrieval models, induce rankings over a set of candidate structures through assignment of scores. The subject of this thesis is to exploit this commonality in three different ranking tasks: (1) "Mate-ranking" refers to the task of mining comparable data for SMT domain adaptation through translation-based CLIR. "Cross-lingual mates" are direct or close translations of the query. I will show that such a CLIR system is able to find in-domain comparable data from noisy user-generated corpora and improves in-domain translation performance of an SMT system. Conversely, the CLIR system relies itself on a translation model that is tailored for retrieval. This leads to the second direction of research, in which I develop two ways to optimize an SMT model for retrieval, namely (2) by SMT parameter optimization towards a retrieval objective ("translation ranking"), and (3) by presenting a joint model of translation and retrieval for "document ranking". The latter abandons the common architecture of modeling both components separately. The former task refers to optimizing for preference of translation candidates that work well for retrieval. In the core task of "document ranking" for CLIR, I present a model that directly ranks documents using an SMT decoder. I present substantial improvements over state-of-the-art translation-based CLIR baseline systems, indicating that a joint model of translation and retrieval is a promising direction of research in the field of CLIR