Evgeny Matusov

Bio: Evgeny Matusov is an academic researcher from eBay. The author has contributed to research in topics: Machine translation & Example-based machine translation. The author has an hindex of 25, co-authored 70 publications receiving 2018 citations. Previous affiliations of Evgeny Matusov include Philips & RWTH Aachen University.

Computing Consensus Translation for Multiple Machine Translation Systems Using Enhanced Hypothesis Alignment

Abstract: This paper describes a novel method for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The outputs are combined and a possibly new translation hypothesis can be generated. Similarly to the well-established ROVER approach of (Fiscus, 1997) for combining speech recognition hypotheses, the consensus translation is computed by voting on a confusion network. To create the confusion network, we produce pairwise word alignments of the original machine translation hypotheses with an enhanced statistical alignment algorithm that explicitly models word reordering. The context of a whole document of translations rather than a single sentence is taken into account to produce the alignment. The proposed alignment and voting approach was evaluated on several machine translation tasks, including a large vocabulary task. The method was also tested in the framework of multi-source and speech translation. On all tasks and conditions, we achieved significant improvements in translation quality, increasing e. g. the BLEU score by as much as 15% relative.

Automatic Text Correction

Abstract: The present invention provides a method of generating text transformation rules for speech to text transcription systems. The text transformation rules are generated by means of comparing an erroneous text generated by a speech to text transcription system with a correct reference text. Comparison of erroneous and reference text allows to derive a set of text transformation rules that are evaluated by means of a strict application to the training text and successive comparison with the reference text. Evaluation of text transformation rules provides a sufficient approach to determine which of the automatically generated text transformation rules provide an enhancement or degradation of the erroneous text. In this way only those text transformation rules of the set of text transformation rules are selected that guarantee an enhancement of the erroneous text. In this way systematic errors of an automatic speech recognition or natural language process system can be effectively compensated.

Automatic sentence segmentation and punctuation prediction for spoken language translation.

Abstract: This paper studies the impact of automatic sentence segmentation and punctuation prediction on the quality of machine translation of automatically recognized speech. We present a novel sentence segmentation method which is specifically tailored to the requirements of machine translation algorithms and is competitive with state-of-the-art approaches for detecting sentence-like units. We also describe and compare three strategies for predicting punctuation in a machine translation framework, including the simple and effective implicit punctuation generation by a statistical phrase-based machine translation system. Our experiments show the robust performance of the proposed sentence segmentation and punctuation prediction approaches on the IWSLT Chinese-to-English and TC-STAR English-to-Spanish speech translation tasks in terms of translation quality.

Novel Reordering Approaches in Phrase-Based Statistical Machine Translation

Abstract: This paper presents novel approaches to reordering in phrase-based statistical machine translation. We perform consistent reordering of source sentences in training and estimate a statistical translation model. Using this model, we follow a phrase-based monotonic machine translation approach, for which we develop an efficient and flexible reordering framework that allows to easily introduce different reordering constraints. In translation, we apply source sentence reordering on word level and use a reordering automaton as input. We show how to compute reordering automata on-demand using IBM or ITG constraints, and also introduce two new types of reordering constraints. We further add weights to the reordering automata. We present detailed experimental results and show that reordering significantly improves translation quality.

Evaluating machine translation output with automatic sentence segmentation.

Abstract: This paper presents a novel automatic sentence segmentation method for evaluating machine translation output with possibly erroneous sentence boundaries. The algorithm can process translation hypotheses with segment boundaries which do not correspond to the reference segment boundaries, or a completely unsegmented text stream. Thus, the method is especially useful for evaluating translations of spoken language. The evaluation procedure takes advantage of the edit distance algorithm and is able to handle multiple reference translations. It efficiently produces an optimal automatic segmentation of the hypotheses and thus allows application of existing well-established evaluation measures. Experiments show that the evaluation measures based on the automatically produced segmentation correlate with the human judgement at least as well as the evaluation measures which are based on manual sentence boundaries.

Moses: Open Source Toolkit for Statistical Machine Translation

Abstract: We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.

Intelligent Automated Assistant

Abstract: An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Six Challenges for Neural Machine Translation.

Abstract: We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

Introduction to Arabic Natural Language Processing

Abstract: he Arabic language has recently become the focus of an increasing number of projects in natural language processing (NLP) and computational linguistics (CL). In this book, I try to provide NLP/CL system developers and researchers (computer scientists and linguists alike) with the necessary background information for working with Arabic.I discuss various Arabic linguistic phenomena and review the state-of-the-art in Arabic processing.

Using context information to facilitate processing of commands in a virtual assistant

Abstract: A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.