Articles

This paper presents preliminary work on identification of argumentation schemes, i.e., identifying premises, conclusion and name of argumentation scheme, in argu- ments for scientific claims in genetics re- search articles. The goal is to develop annotation guidelines for creating corpo- ra for argumentation mining research. This paper gives the specification …...

This paper presents the results of an annota- tion study focused on the fine-grained analysis of argumentation structures in scientific pub- lications. Our new annotation scheme spec- ifies four types of binary argumentative rela- tions between sentences, resulting in the rep- resentation of arguments as small graph struc- tures. We …...

This article describes the Aalto Uni- versity entry to the English-to-Finnish shared translation task in WMT 2015. The system participates in the con- strained condition, but in addition we impose some further constraints, using no language-specific resources beyond those provided in the task. We use a morphological segmenter, Morfessor FlatCat, …...

This paper presents the results of the WMT15 Metrics Shared Task. We asked participants of this task to score the out- puts of the MT systems involved in the WMT15 Shared Translation Task. We col- lected scores of 46 metrics from 11 re- search groups. In addition to that, we …...

The log-linear combination of different features is an important component of SMT systems. It allows for the easy in- tegartion of models into the system and is used during decoding as well as for n- best list rescoring. With the recent suc- cess of more complex models like neural network-based …...

An important limitation of automatic eval- uation metrics is that, when comparing Machine Translation (MT) to a human ref- erence, they are often unable to discrimi- nate between acceptable variation and the differences that are indicative of MT er- rors. In this paper we present UPF-Cobalt evaluation system that addresses …...

Translations generated by current statisti- cal systems often have a large variance, in terms of their quality against human ref- erences. To cope with such variation, we propose to evaluate translations using a multi-level framework. The method varies the evaluation criteria based on the clus- ters to which a translation …...

This paper describes the submission of the UGENT-LT3 SCATE system to the WMT15 Shared Task on Quality Estima- tion (QE), viz. English-Spanish word and sentence-level QE. We conceived QE as a supervised Machine Learning (ML) problem and designed additional features and combined these with the baseline feature set to estimate …...

This paper introduces our SAU-KERC system that achieved F1 score of 0.39 in the world-level quality estimation task in WMT2015. The goal is to assign each trans- lated word a “OK” or “BAD” label indicating translation quality. We adopt the sequence labeling model, conditional random fields (CRF), to predict the …...

We describe our systems for Tasks 1 and 2 of the WMT15 Shared Task on Qual- ity Estimation. Our submissions use (i) a continuous space language model to ex- tract additional features for Task 1 (SHEF- GP, SHEF-SVM), (ii) a continuous bag- of-words model to produce word embed- dings as …...

We present the results of the USHEF and USAAR-USHEF submissions for the WMT15 shared task on document-level quality estimation. The USHEF sub- missions explored several document and discourse-aware features. The USAAR- USHEF submissions used an exhaustive search approach to select the best features from the official baseline. Results show slight …...

This paper describes the DCU-SHEFF word-level Quality Estimation (QE) sys- tem submitted to the QE shared task at WMT15. Starting from a baseline set of features and a CRF algorithm to learn a sequence tagging model, we propose im- provements in two ways: (i) by filtering out the training sentences …...

We describe our system for WMT2015 Shared Task on Quality Estimation, task 1, sentence-level prediction of post-edition effort. We use baseline features, Latent Semantic Indexing based features and fea- tures based on pseudo-references. SVM algorithm allows to estimate the linear re- gression between the features vectors and the HTER score. …...

This paper describes VERTa’s submis- sion to the 2015 EMNLP Workshop on Statistical Machine Translation. VERTa is a linguistically-motivated metric that combines linguistic features at different levels. In this paper, VERTa is described briefly, as well as the three versions submitted to the workshop: VERTa- 70Adeq30Flu, VERTa-EQ and VERTa- W. …...

This paper describes the system submit- ted by the University of Heidelberg to the Shared Task on Word-level Quality Esti- mation at the 2015 Workshop on Statis- tical Machine Translation. The submit- ted system combines a continuous space deep neural network, that learns a bilin- gual feature representation from scratch, …...

This paper describes the Universitat d’Alacant submissions (labelled as UAla- cant) for the machine translation quality estimation (MTQE) shared task in WMT 2015, where we participated in the word- level MTQE sub-task. The method we used to produce our submissions uses external sources of bilingual information as a black box …...

This paper presents the results of the WMT15 Tuning Shared Task. We pro- vided the participants of this task with a complete machine translation system and asked them to tune its internal parameters (feature weights). The tuned systems were used to translate the test set and the out- puts were …...

We propose a novel extended translation model (ETM) to counteract some prob- lems in phrase-based translation: The lack of translation context when using single- word phrases and uncaptured dependen- cies beyond phrase boundaries. The ETM operates on word-level and augments the IBM models by an additional bilingual word pair and …...

This work explores the application of re- current neural network (RNN) language and translation models during phrase- based decoding. Due to their use of un- bounded context, the decoder integration of RNNs is more challenging compared to the integration of feedforward neural mod- els. In this paper, we apply approxima- …...

We present a hierarchical statistical ma- chine translation system which supports discontinuous constituents. It is based on synchronous linear context-free rewriting systems (SLCFRS), an extension to syn- chronous context-free grammars in which synchronized non-terminals span k ≥ 1 continuous blocks on either side of the bitext. This extension beyond context- …...