We study the performance of machine learning techniques to the problem of identifying speakers at meetings from anonymous minutes issued afterwards. The data comes from board meetings of Sveriges Riksbank (Sweden’s Central Bank). The data is split in two ways, one where each reported contribution to the discussion is treated as a data point, and another where all contributions from a single speaker have been aggregated. Using interpretable models we find that lexical features and topic models generated from speeches held by the board members outside of board meetings are good predictors of speaker identity. Combining topic models with other features gives prediction accuracies close to 80% on aggregated data, though there is still a sizeable gap in performance compared to a not easily interpreted BERT-based transformer model that we offer as a benchmark.

As machine translation technology improves comparisons to human performance are often made in quite general and exaggerated terms. Thus, it is important to be able to account for differences accurately. This paper reports a simple, descriptive scheme for comparing translations and applies it to two translations of a British opinion article published in March, 2017. One is a human translation (HT) into Swedish, and the other a machine translation (MT). While the comparison is limited to one text, the results are indicative of current limitations in MT.

Error analysis is a means to assess machine translation output in qualitative terms, which can be used as a basis for the generation of error profiles for different systems. As for other subjective approaches to evaluation it runs the risk of low inter-annotator agreement, but very often in papers applying error analysis to MT, this aspect is not even discussed. In this paper, we report results from a comparative evaluation of two systems where agreement initially was low, and discuss the different ways we used to improve it. We compared the effects of using more or less fine-grained taxonomies, and the possibility to restrict analysis to short sentences only. We report results on inter-annotator agreement before and after measures were taken, on error categories that are most likely to be confused, and on the possibility to establish error profiles also in the absence of a high inter-annotator agreement.

We present a study of Polish-English machine translation, where the impact of various types of errors on cohesion and comprehensibility of the translations were investigated. The following phenomena are in focus: (i) The most common errors produced by current state-of-the-art MT systems for Polish-English MT. (ii) The effect of different types of errors on text cohesion. (iii) The effect of different types of errors on readers' understanding of the translation. We found that errors of incorrect and missing translations are the most common for current systems, while the category of non-translated words had the most negative impact on comprehension. All three of these categories contributed to the breaking of cohesive chains. The correlation between number of errors found in a translation and number of wrong answers in the comprehension tests was low. Another result was that non-native speakers of English performed at least as good as native speakers on the comprehension tests.

We present a method for improving word alignment quality for phrase-based statistical machine translation by reordering the source text according to the target word order suggested by an initial word alignment. The reordered text is used to create a second word alignment which can be an improvement of the first alignment, since the word order is more similar. The method requires no other pre-processing such as part-of-speech tagging or parsing. We report improved Bleu scores for English-to-German and English-to-Swedish translation. We also examined the effect on word alignment quality and found that the reordering method increased recall while lowering precision, which partly can explain the improved Bleu scores. A manual evaluation of the translation output was also performed to understand what effect our reordering method has on the translation system. We found that where the system employing reordering differed from the baseline in terms of having more words, or a different word order, this generally led to an improvement in translation quality.

This paper profiles the Europarl part of an English-Swedish parallel corpus and compares it with three other subcorpora of the same parallel corpus. We first describe our method for comparison which is based on manually reviewed word alignments. We investigate relative frequences of different types of correspondence, including null alignments, many-to-one correspondences and crossings. In addition, both halves of the parallel corpus have been annotated with morpho-syntactic information. The syntactic annotation uses labelled dependency relations. Thus, we can see how different types of correspondences are distributed on different parts-of-speech and compute correspondences at the structural level. In spite of the fact that two of the other subcorpora contains fiction, it is found that the Europarl part is the one having the highest proportion of many types of restructurings, including additions, deletions, long distance reorderings and dependency reversals. We explain this by the fact that the majority of Europarl segments are parallel translations rather than source texts and their translations.

One problem in statistical machine translation (SMT) is that the output often is ungrammatical. To address this issue, we have investigated the use of a grammar checker for two purposes in connection with SMT: as an evaluation tool and as a postprocessing tool. To assess the feasibility of the grammar checker on SMT output, we performed an error analysis, which showed that the precision of error identification in general was higher on SMT output than in previous studies on human texts. Using the grammar checker as an evaluation tool gives a complementary picture to standard metrics such as Bleu, which do not account well for grammaticality. We use the grammar checker as a postprocessing tool by automatically applying the error correction suggestions it gives. There are only small overall improvements of the postprocessing on automatic metrics, but the sentences that are affected by the changes are improved, as shown both by automatic metrics and by a human error analysis. These results indicate that grammar checker techniques are a useful complement to SMT.

This paper describes a syllabification based conversion method for converting romanized Persian text to the traditional Arabic-based writing system. The system is implemented in Xerox XFST and relies on rule based conversion of words rather than using morphological analysis. The paper presents a brief evaluation of the accuracy of the transcriptions generated by the method.

KUNSTI is the Norwegian national language technology programme, running 2001-2006 inclusive. The goal of the programme is to boost Norwegian language technology research. In this paper we describe the background, the objectives, the methodology applied in the management of the programme, the projects selected, and our first conclusions. We also describe national programmes form Sweden, France and Germany and compare objectives and methods.