Articles

Election manifestos document the inten- tions, motives, and views of political par- ties. They are often used for analysing party policies and positions on various is- sues, as well as for quantifying a party’s position on the left–right spectrum. In this paper we propose a model for auto- matically predicting …...

This paper describes the system details and results of team “EOF” from the Uni- versity of Melbourne for the shared task of ALTA 2017, which addresses the problem of text correction for post-processed Op- tical Character Recognition (OCR) based systems. We developed a two stage sys- tem which first detects …...

This paper presents an overview of the 8th ALTA shared task that ran in 2017. The task was to correct OCR errors from scans of newspapers stored in the Trove database maintained by the National Li- brary of Australia. We introduce the task, describe the data and present the results …...

This paper presents an empirical com- parison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unseg- mented text: (i) a pipeline strategy where we consider the output of a word seg- menter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined …...

Supervised approaches for text summari- sation suffer from the problem of mis- match between the target labels/scores of individual sentences and the evaluation score of the final summary. Reinforcement learning can solve this problem by pro- viding a learning mechanism that uses the score of the final summary as a …...

Extracting information from semi- structured text has been studied only for limited domain sources due to its hetero- geneous formats. This paper proposes a Ripple-Down Rules (RDR) based ap- proach to extract relations from both semi- structured and unstructured text in open domain Web pages. We find that RDR's 'case-by-case' …...

The WMT17 Neural Machine Translation Training Task aims to test various methods of training neural machine translation sys- tems. We describe the AFRL submission, including preprocessing and its knowledge distillation framework. Teacher systems are given factors for domain, case, and subword location. Student systems are given multiple teachers’ output and …...

This paper describes our submission to the WMT 2017 Neural MT Training Task. We modified the provided NMT system in order to allow for interrupting and con- tinuing the training of models. This al- lowed mid-training batch size decremen- tation and incrementation at variable rates. In addition to the models …...

This paper describes LIMSI participation to the WMT’17 shared task on Bandit Learning. The method we propose to adapt a seed system trained on out-domain data to a new, unknown domain relies on two components. First, we use a linear regression model to exploit the weak and partial feedback the …...

We describe the University of Mary- land machine translation systems submit- ted to the WMT17 German-English Ban- dit Learning Task. The task is to adapt a translation system to a new domain, using only bandit feedback: the system receives a German sentence to translate, produces an English sentence, and only …...

Following upon the last year’s CUNI sys- tem for automatic post-editing of machine translation output, we focus on exploit- ing the potential of sequence-to-sequence neural models for this task. In this sys- tem description paper, we compare sev- eral encoder-decoder architectures on a smaller-scale models and present the sys- tem …...

Automatic post-editing (APE) is a challenging task on WMT evaluation campaign. We find that only a small number of edit operations are required for most machine translation outputs, through analysis of the training set of WMT17 APE en-de task. Based on this statistics analysis, two neural post- editing (NPE) models …...

This work presents a novel approach to Automatic Post-Editing (APE) and Word- Level Quality Estimation (QE) using en- sembles of specialized Neural Machine Translation (NMT) systems. Word-level features that have proven effective for QE are included as input factors, expanding the representation of the original source and the machine translation …...

This work describes the AMU-UEdin sub- mission to the WMT 2017 shared task on Automatic Post-Editing. We explore mul- tiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both in- puts mt and src in a single …...

Previous phrase-based approaches to Au- tomatic Post-editing (APE) have shown that the dependency of MT errors from the source sentence can be exploited by jointly learning from source and target informa- tion. By integrating this notion in a neu- ral approach to the problem, we present the multi-source neural machine …...

This paper presents the LIG-CRIStAL submission to the shared Automatic Post- Editing task of WMT 2017. We propose two neural post-editing models: a mono- source model with a task-specific atten- tion mechanism, which performs particu- larly well in a low-resource scenario; and a chained architecture which makes use of the …...

Character n-gram F-score (CHRF) shown to correlate very well with hu-is man relative rankings of different machine translation outputs, especially for morpho- logically rich target languages. However, its relation with direct human assessments is not yet clear. In this work, Pearson’s correlation coefficients for direct assess- ments are investigated for …...

In this paper, we propose three different methods for automatic evaluation of the machine translation (MT) quality. Two of the metrics are trainable on direct- assessment scores and two of them use de- pendency structures. The trainable metric AutoDA, which uses deep-syntactic fea- tures, achieved better correlation with hu- mans …...

Existing metrics to evaluate the quality of Machine Translation hypotheses take dif- ferent perspectives into account. DPM- Fcomb, a metric combining the merits of a range of metrics, achieved the best performance for evaluation of to-English language pairs in the previous two years of WMT Metrics Shared Tasks. This year, …...

We describe a new version of MEANT, which participated in the metrics task of the Second Conference on Machine Trans- lation (WMT 2017). MEANT 2.0 uses idf- weighted distributional ngram accuracy to determine the phrasal similarity of seman- tic role fillers and yields better correlations with human judgments of translation …...