Measuring Syntactic Complexity in Spoken and Written Learner Language: Comparing the Incomparable?

Spoken and written language are two modes of language. When learners aim at higher skill levels, the expected outcome of successful second language learning is usually to become a fluent speaker and writer who can produce accurate and complex language in the target language. There is an axiomatic difference between speech and writing, but together they form the essential parts of learners’ L2 skills. The two modes have their own characteristics, and there are differences between native and nonnative language use. For instance, hesitations and pauses are not visible in the end result of the writing process, but they are characteristic of nonnative spoken language use. The present study is based on the analysis of L2 English spoken and written productions of 18 L1 Finnish learners with focus on syntactic complexity. As earlier spoken language segmentation units mostly come from fluency studies, we conducted an experiment with a new unit, the U-unit, and examined how using this unit as the basis of spoken language segmentation affects the results. According to the analysis, written language was more complex than spoken language. However, the difference in the level of complexity was greatest when the traditional units, T-units and AS-units, were used in segmenting the data. Using the U-unit revealed that spoken language may, in fact, be closer to written language in its syntactic complexity than earlier studies had suggested. Therefore, further research is needed to discover whether the differences in spoken and written learner language are primarily due to the nature of these modes or, rather, to the units and measures used in the analysis

2kenize: Tying Subword Sequences for Chinese Script Conversion

Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have poor performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification. An error analysis reveals that our method's particular strengths are in dealing with code-mixing and named entities.Comment: Accepted to ACL 202

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project