Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation

Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word embeddings can be applied to this challenging syntactic-semantic task. Our method uses cross-lingual translation pairs to tie each of the six target languages into a bilingual vector space with English, jointly specialising the representations to encode the relational information from English VerbNet. A standard clustering algorithm is then run on top of the VerbNet-specialised representations, using vector dimensions as features for learning verb classes. Our results show that the proposed cross-lingual transfer approach sets new state-of-the-art verb classification performance across all six target languages explored in this work.Comment: EMNLP 2017 (long paper

Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings

Word embeddings have become a standard resource in the toolset of any Natural Language Processing practitioner. While monolingual word embeddings encode information about words in the context of a particular language, cross-lingual embeddings define a multilingual space where word embeddings from two or more languages are integrated together. Current state-of-the-art approaches learn these embeddings by aligning two disjoint monolingual vector spaces through an orthogonal transformation which preserves the structure of the monolingual counterparts. In this work, we propose to apply an additional transformation after this initial alignment step, which aims to bring the vector representations of a given word and its translations closer to their average. Since this additional transformation is non-orthogonal, it also affects the structure of the monolingual spaces. We show that our approach both improves the integration of the monolingual spaces as well as the quality of the monolingual spaces themselves. Furthermore, because our transformation can be applied to an arbitrary number of languages, we are able to effectively obtain a truly multilingual space. The resulting (monolingual and multilingual) spaces show consistent gains over the current state-of-the-art in standard intrinsic tasks, namely dictionary induction and word similarity, as well as in extrinsic tasks such as cross-lingual hypernym discovery and cross-lingual natural language inference.Comment: 22 pages, 2 figures, 9 tables. Preprint submitted to Natural Language Engineerin

Investigating the foreign language effect as a mitigating influence on the ‘optimality bias’ in moral judgements

Bilinguals often display reduced emotional resonance their second language (L2) and therefore tend to be less prone to decision-making biases in their L2 (e.g., Costa et al. in Cognition 130(2):236–254, 2014a, PLoS One 9(4):1–7, 2014b)—a phenomenon coined Foreign Language Effect (FLE). The present pre-registered experiments investigated whether FLE can mitigate a special case of cognitive bias, called optimality bias, which occurs when observers erroneously blame actors for making “suboptimal” choices, even when there was not sufficient information available for the actor to identify the best choice (De Freitas and Johnson in J Exp Soc Psychol 79:149–163, 2018. https://doi.org/10.1016/j.jesp.2018.07.011). In Experiment 1, L1 English speakers (N = 63) were compared to L2 English speakers from various L1 backgrounds (N = 56). In Experiment 2, we compared Finnish bilinguals completing the study in either Finnish (L1, N = 103) or English (L2, N = 108). Participants read a vignette describing the same tragic outcome resulting from either an optimal or suboptimal choice made by a hypothetical actor with insufficient knowledge. Their blame attributions were measured using a 4-item scale. A strong optimality bias was observed; participants assigned significantly more blame in the suboptimal choice conditions, despite being told that the actor did not know which choice was best. However, no clear interaction with language was found. In Experiment 1, bilinguals gave reliably higher blame scores than natives. In Experiment 2, no clear influence of target language was found, but the results suggested that the FLE is actually more detrimental than helpful in the domain of blame attribution. Future research should investigate the benefits of emotional involvement in blame attribution, including factors such as empathy and perspective-taking

Valuing All Languages in Europe

The VALEUR project (2004-2007) took as its focus the 'additional' languages of Europe. These are defined as all languages in use in contexts where they are not 'national', 'official', or 'dominant' languages. They include 'migrant' languages, 'regional/minority' languages, sign languages and 'non-territorial' languages of diasporas such as Yiddish and Romani. The project team brought together a range of expertise in sociolinguistics and language pedagogy, planning and research from Finland, Netherlands, Poland, Spain and the UK. We took as our starting point Council of Europe policies on plurilingualism and the desirability of promoting linguistic diversity both for individual citizenship and for social cohesion in Europe. Our aim was to map provision for additional languages in Europe, in a more systematic and inclusive way than ever before. We looked at provision at school level for different languages in different contexts in order to identify good practices to be shared. In order to achieve our objectives we drew on the good will and enthusiasm of workshop participants, who provided a wealth of information and insights from 21 of the Council of Europe member states. Our work is not definitive: its purpose is awareness-raising and to stimulate further activity to support the learning of all Europe's languages