Exact decoding for phrase-based statistical machine translation

© 2014 Association for Computational Linguistics. The combinatorial space of translation derivations in phrase-based statistical machine translation is given by the intersection between a translation lattice and a target language model. We replace this intractable intersection by a tractable relaxation which incorporates a low-order upperbound on the language model. Exact optimisation is achieved through a coarseto- fine strategy with connections to adaptive rejection sampling. We perform exact optimisation with unpruned language models of order 3 to 5 and show searcherror curves for beam search and cube pruning on standard test sets. This is the first work to tractably tackle exact optimisation with language models of orders higher than 3

Joint Emotion Analysis via Multi-task Gaussian Processes

We propose a model for jointly predicting multiple emotions in natural language sentences. Our model is based on a low-rank coregionalisation approach, which combines a vector-valued Gaussian Process with a rich parameterisation scheme. We show that our approach is able to learn correlations and anti-correlations between emotions on a news headlines dataset. The proposed model outperforms both singletask baselines and other multi-task approaches

Semantic modelling of user interests based on cross-folksonomy analysis

The continued increase in Web usage, in particular participation in folksonomies, reveals a trend towards a more dynamic and interactive Web where individuals can organise and share resources. Tagging has emerged as the de-facto standard for the organisation of such resources, providing a versatile and reactive knowledge management mechanism that users find easy to use and understand. It is common nowadays for users to have multiple profiles in various folksonomies, thus distributing their tagging activities. In this paper, we present a method for the automatic consolidation of user profiles across two popular social networking sites, and subsequent semantic modelling of their interests utilising Wikipedia as a multi-domain model. We evaluate how much can be learned from such sites, and in which domains the knowledge acquired is focussed. Results show that far richer interest profiles can be generated for users when multiple tag-clouds are combine

Semantics, sensors, and the social web: The live social semantics experiments

The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data and technologies from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities at conferences. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes this application, and discusses and evaluates the results of its two deployment

Towards Optimizing MT for Post-Editing Effort: Can BLEU Still Be Useful?

We propose a simple, linear-combination automatic evaluation measure (AEM) to approximate post-editing (PE) effort. Effort is measured both as PE time and as the number of PE operations performed. The ultimate goal is to define an AEM that can be used to optimize machine translation (MT) systems to minimize PE effort, but without having to perform unfeasible repeated PE during optimization. As PE effort is expected to be an extensive magnitude (i.e., one growing linearly with the sentence length and which may be simply added to represent the effort for a set of sentences), we use a linear combination of extensive and pseudo-extensive features. One such pseudo-extensive feature, 1–BLEU times the length of the reference, proves to be almost as good a predictor of PE effort as the best combination of extensive features. Surprisingly, effort predictors computed using independently obtained reference translations perform reasonably close to those using actual post-edited references. In the early stage of this research and given the inherent complexity of carrying out experiments with professional post-editors, we decided to carry out an automatic evaluation of the AEMs proposed rather than a manual evaluation to measure the effort needed to post-edit the output of an MT system tuned on these AEMs. The results obtained seem to support current tuning practice using BLEU, yet pointing at some limitations. Apart from this intrinsic evaluation, an extrinsic evaluation was also carried out in which the AEMs proposed were used to build synthetic training corpora for MT quality estimation, with results comparable to those obtained when training with measured PE efforts.Work supported by the Spanish government through project EFFORTUNE (TIN2015-69632-R) and through grant PRX16/00043 for Mikel L. Forcada, and by the European Commission through QT21 project (H2020 No. 645452)

Evaluating the semantic web: a task-based approach

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape

A Bayesian non-linear method for feature selection in machine translation quality estimation

We perform a systematic analysis of the effectiveness of features for the problem of predicting the quality of machine translation (MT) at the sentence level. Starting from a comprehensive feature set, we apply a technique based on Gaussian processes, a Bayesian non-linear learning method, to automatically identify features leading to accurate model performance. We consider application to several datasets across different language pairs and text domains, with translations produced by various MT systems and scored for quality according to different evaluation criteria. We show that selecting features with this technique leads to significantly better performance in most datasets, as compared to using the complete feature sets or a state-of-the-art feature selection approach. In addition, we identify a small set of features which seem to perform well across most datasets

Towards predicting post-editing productivity

Machine translation (MT) quality is generally measured via automatic metrics, producing scores that have no meaning for translators who are required to post-edit MT output or for project managers who have to plan and budget for transla- tion projects. This paper investigates correlations between two such automatic metrics (general text matcher and translation edit rate) and post-editing productivity. For the purposes of this paper, productivity is measured via processing speed and cognitive measures of effort using eye tracking as a tool. Processing speed, average fixation time and count are found to correlate well with the scores for groups of segments. Segments with high GTM and TER scores require substantially less time and cognitive effort than medium or low-scoring segments. Future research involving score thresholds and confidence estimation is suggested

Semantic disambiguation and contextualisation of social tags

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-28509-7_18This manuscript is an extended version of the paper ‘cTag: Semantic Contextualisation of Social Tags’, presented at the 6th International Workshop on Semantic Adaptive Social Web (SASWeb 2011).We present an algorithmic framework to accurately and efficiently identify the semantic meanings and contexts of social tags within a particular folksonomy. The framework is used for building contextualised tag-based user and item profiles. We also present its implementation in a system called cTag, with which we preliminary analyse semantic meanings and contexts of tags belonging to Delicious and MovieLens folksonomies. The analysis includes a comparison between semantic similarities obtained for pairs of tags in Delicious folksonomy, and their semantic distances in the whole Web, according to co-occurrence based metrics computed with results of a Web search engine.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and Universidad Autónoma de Madrid (CCG10-UAM/TIC-5877

The USFD Spoken Language Translation System for IWSLT 2014

The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data