Increasing Social Integration in an Interdisciplinary MA Programme through Group Work

In an interdisciplinary MA programme, it is especially important that the students get socially integrated from the beginning. Most often not only the place and people will be new but also the field of study. This can be difficult to handle without a network. In this project I will investigate using group work to help initiate social integration. In this context, I will also reflect on the different group work I conducted

syntactic recordering in statistical machine translation

Reordering has been an important topic in statistical machine translation (SMT) as long as SMT has been around. State-of-the-art SMT systems such as Pharaoh (Koehn, 2004a) still employ a simplistic model of the reordering process to do non-local reordering. This model penalizes any reordering no matter the words. The reordering is only selected if it leads to a translation that looks like a much better sentence than the alternative. Recent developments have, however, seen improvements in translation quality following from syntax-based reordering. One such development is the pre-translation approach that adjusts the source sentence to resemble target language word order prior to translation. This is done based on rules that are either manually created or automatically learned from word aligned parallel corpora. We introduce a novel approach to syntactic reordering. This approach provides better exploitation of the information in the reordering rules and eliminates problematic biases of previous approaches. Although the approach is examined within a pre-translation reordering framework, it easily extends to other frameworks. Our approach significantly outperforms a state-of-the-art phrase-based SMT system and previous approaches to pretranslation reordering, including (Li et al., 2007; Zhang et al., 2007b; Crego & Mari˜ no, 2007). This is consistent both for a very close language pair, English-Danish, and a very distant language pair, English-Arabic. We also propose automatic reordering rule learning based on a rich set of linguistic information. As opposed to most previous approaches that extract a large set of rules, our approach produces a small set of predominantly general rules. These provide a good reflection of the main reordering issues of a given language pair. We examine the influence of several parameters that may have influence on the quality of the rules learned. Finally, we provide a new approach for improving automatic word alignment. This word alignment is used in the above task of automatically learning reordering rules. Our approach learns from hand aligned data how to combine several automatic word alignments to one superior word alignment. The automatic word alignments are created from the same data that has been preprocessed with different tokenization schemes. Thus utilizing the different strengths that different tokenization schemes exhibit in word alignment. We achieve a 38% error reduction for the automatic word alignmen

Incremental Re-training for Post-editing SMT

A method is presented for incremental retraining of an SMT system, in which a local phrase table is created and incrementally updated as a file is translated and post-edited. It is shown that translation data from within the same file has higher value than other domain-specific data. In two technical domains, within-file data increases BLEU score by several full points. Furthermore, a strong recency effect is documented; nearby data within the file has greater value than more distant data. It is also shown that the value of translation data is strongly correlated with a metric defined over new occurrences of ngrams. Finally, it is argued that the incremental re-training prototype could serve as the basis for a practical system which could be interactively updated in real time in a post-editing setting. Based on the results here, such an interactive system has the potential to dramatically improve translation quality

Effect of implantable cardioverter-defibrillators in patients with non-ischaemic systolic heart failure and concurrent coronary atherosclerosis

AIMS: Prophylactic implantable cardioverter‐defibrillators (ICD) reduce mortality in patients with ischaemic heart failure (HF), whereas the effect of ICD in patients with non‐ischaemic HF is less clear. We aimed to investigate the association between concomitant coronary atherosclerosis and mortality in patients with non‐ischaemic HF and the effect of ICD implantation in these patients. METHODS AND RESULTS: Patients were included from DANISH (Danish Study to Assess the Efficacy of Implantable Cardioverter Defibrillators in Patients with Non‐Ischaemic Systolic Heart Failure on Mortality), randomizing patients to ICD or control. Study inclusion criteria for HF were left ventricular ejection fraction ≤ 35% and increased levels (>200 pg/mL) of N‐terminal pro‐brain natriuretic peptide. Of the 1116 patients from DANISH, 838 (75%) patients had available data from coronary angiogram and were included in this subgroup analysis. We used Cox regression to assess the relationship between coronary atherosclerosis and mortality and the effect of ICD implantation. Of the included patients, 266 (32%) had coronary atherosclerosis. Of these, 216 (81%) had atherosclerosis without significant stenoses, and 50 (19%) had significant stenosis. Patients with atherosclerosis were significantly older {67 [interquartile range (IQR) 61–73] vs. 61 [IQR 54–68] years; P < 0.0001}, and more were men (77% vs. 70%; P = 0.03). During a median follow‐up of 64.3 months (IQR 47–82), 174 (21%) of the patients died. The effect of ICD on all‐cause mortality was not modified by coronary atherosclerosis [hazard ratio (HR) 0.94; 0.58–1.52; P = 0.79 vs. HR 0.82; 0.56–1.20; P = 0.30], P for interaction = 0.67. In univariable analysis, coronary atherosclerosis was a significant predictor of all‐cause mortality [HR, 1.41; 95% confidence interval (CI), 1.04–1.91; P = 0.03]. However, this association disappeared when adjusting for cardiovascular risk factors (age, gender, diabetes, hypertension, smoking, and estimated glomerular filtration rate) (HR 1.05, 0.76–1.45, P = 0.76). CONCLUSIONS: In patients with non‐ischaemic systolic heart failure, ICD implantation did not reduce all‐cause mortality in patients either with or without concomitant coronary atherosclerosis. The concomitant presence of coronary atherosclerosis was associated with increased mortality. However, this association was explained by other risk factors

Syntactic reordering in statistical machine translation

Reordering has been an important topic in statistical machine translation (SMT) as long as SMT has been around. State-of-the-art SMT systems such as Pharaoh (Koehn, 2004a) still employ a simplistic model of the reordering process to do non-local reordering. This model penalizes any reordering no matter the words. The reordering is only selected if it leads to a translation that looks like a much better sentence than the alternative. Recent developments have, however, seen improvements in translation quality following from syntax-based reordering. One such development is the pre-translation approach that adjusts the source sentence to resemble target language word order prior to translation. This is done based on rules that are either manually created or automatically learned from word aligned parallel corpora. We introduce a novel approach to syntactic reordering. This approach provides better exploitation of the information in the reordering rules and eliminates problematic biases of previous approaches. Although the approach is examined within a pre-translation reordering framework, it easily extends to other frameworks. Our approach significantly outperforms a state-of-the-art phrase-based SMT system and previous approaches to pretranslation reordering, including (Li et al., 2007; Zhang et al., 2007b; Crego & Mari˜ no, 2007). This is consistent both for a very close language pair, English-Danish, and a very distant language pair, English-Arabic. We also propose automatic reordering rule learning based on a rich set of linguistic information. As opposed to most previous approaches that extract a large set of rules, our approach produces a small set of predominantly general rules. These provide a good reflection of the main reordering issues of a given language pair. We examine the influence of several parameters that may have influence on the quality of the rules learned. Finally, we provide a new approach for improving automatic word alignment. This word alignment is used in the above task of automatically learning reordering rules. Our approach learns from hand aligned data how to combine several automatic word alignments to one superior word alignment. The automatic word alignments are created from the same data that has been preprocessed with different tokenization schemes. Thus utilizing the different strengths that different tokenization schemes exhibit in word alignment. We achieve a 38% error reduction for the automatic word alignmen

Computational Linguistics

We present a novel approach to word reordering which successfully integrates syntactic structural knowledge with phrase-based SMT. This is done by constructing a lattice of alternatives based on automatically learned probabilistic syntactic rules. In decoding, the alternatives are scored based on the output word order, not the order of the input. Unlike previous approaches, this makes it possible to successfully integrate syntactic reordering with phrase-based SMT. On an English-Danish task, we achieve an absolute improvement in translation quality of 1.1 % BLEU. Manual evaluation supports the claim that the present approach is significantly superior to previous approaches.