This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries

/pdf/findings-of-the-2014-workshop-on-statistical-machine-2c6x8ivq8b.pdf

Findings of the 2014 Workshop on Statistical Machine Translation

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.

/pdf/findings-of-the-2016-conference-on-machine-translation-1l1b0qfzdj.pdf

Findings of the 2016 Conference on Machine Translation

Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.

/pdf/synthetic-and-natural-noise-both-break-neural-machine-41z8rg2kva.pdf

Synthetic and Natural Noise Both Break Neural Machine Translation

This paper presents the results of the WMT12 shared tasks, which included a translation task, a task for machine translation evaluation metrics, and a task for run-time estimation of machine translation quality. We conducted a large-scale manual evaluation of 103 machine translation systems submitted by 34 teams. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 12 evaluation metrics. We introduced a new quality estimation task this year, and evaluated submissions from 11 teams.

/pdf/findings-of-the-2012-workshop-on-statistical-machine-1ph6stnr4w.pdf

Findings of the 2012 Workshop on Statistical Machine Translation

Unbiased language is a requirement for reference sources like encyclopedias and scientific texts. Bias is, nonetheless, ubiquitous, making it crucial to understand its nature and linguistic realization and hence detect bias automatically. To this end we analyze real instances of human edits designed to remove bias from Wikipedia articles. The analysis uncovers two classes of bias: framing bias, such as praising or perspective-specific words, which we link to the literature on subjectivity; and epistemological bias, related to whether propositions that are presupposed or entailed in the text are uncontroversially accepted as true. We identify common linguistic cues for these classes, including factive verbs, implicatives, hedges, and subjective intensifiers. These insights help us develop features for a model to solve a new prediction task of practical importance: given a biased sentence, identify the bias-inducing word. Our linguistically-informed model performs almost as well as humans tested on the same task.

/pdf/linguistic-models-for-analyzing-and-detecting-biased-5f50dhtcgj.pdf

Linguistic Models for Analyzing and Detecting Biased Language

Naturally-occurring instances of linguistic phenomena are important both for training and for evaluating automatic text processing. When available in large quantities, they also prove interesting material for linguistic studies. In this article, we present WiCoPaCo (Wikipedia Correction and Paraphrase Corpus), a new freely-available resource built by automatically mining Wikipedias revision history. The WiCoPaCo corpus focuses on local modifications made by human revisors and include various types of corrections (such as spelling error or typographical corrections) and rewritings, which can be categorized broadly into meaning-preserving and meaning-altering revisions. We present an initial hand-built typology of these revisions, but the resource allows for any possible annotation scheme. We discuss the main motivations for building such a resource and describe the main technical details guiding its construction. We also present applications and data analysis on French and report initial results on spelling error correction and morphosyntactic rewriting. The WiCoPaCo corpus can be freely downloaded from http://wicopaco.limsi.fr.

http://www.lrec-conf.org/proceedings/lrec2010/pdf/827_Paper.pdf

Mining Naturally-occurring Corrections and Paraphrases from Wikipedia's Revision History.

We introduce pre-post-editing, possibly the most basic form of interactive translation, as a touch-based interaction with iteratively improved translation hypotheses prior to classical post-editing. We report simulated experiments that yield very large improvements on classical evaluation metrics (up to 21 BLEU) as well as on a parameterized variant of the TER metric that takes into account the cost of matching/touching tokens, confirming the promising prospects of the novel translation scenarios offered by our approach.

/pdf/touch-based-pre-post-editing-of-machine-translation-output-3pkv1l8laq.pdf

Touch-Based Pre-Post-Editing of Machine Translation Output

The Ritel project aims at integrating spoken language dialog and open-domain information retrieval to allow a human to ask general questions (e.g. Who is currently presiding the French Senate?) and refine her search interactively. This project is at the junction of several distinct research communities, and has therefore several challenges to tackle: real-time streamed speech recognition with very large vocabulary, open-domain dialog management, fast information retrieval from text, query cobuilding, communication between information retrieval and dialog components, and generation of natural sounding answers. In this paper, we present our work on the different components of Ritel, and provide initial results.

Integrating Spoken Dialog and Question Answering: the Ritel Project

In this article, an original view on how to improve phrase translation estimates is proposed. This proposal is grounded on two main ideas: first, that appropriate examples of a given phrase should participate more in building its translation distribution; second, that paraphrases can be used to better estimate this distribution. Initial experiments provide evidence of the potential of our approach and its implementation for effectively improving translation performance.

Example-Based Paraphrasing for Improved Phrase-Based Statistical Machine Translation

We present a framework where auxiliary MT systems are used to provide lexical predictions to a main SMT system. In this work, predictions are obtained by means of pivoting via auxiliary languages, and introduced into the main SMT system in the form of a low order language model, which is estimated on a sentence-by-sentence basis. The linear combination of models implemented by the decoder is thus extended with this additional language model. Experiments are carried out over three different translation tasks using the European Parliament corpus. For each task, nine additional languages are used as auxiliary languages to obtain the triangulated predictions. Translation accuracy results show that improvements in translation quality are obtained, even for large data conditions.

http://www.quaero.org/media/files/bibliographie/crego_local_coling2010.pdf

Local lexical adaptation in Machine Translation through triangulation: SMT helping SMT

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