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1. Language of instruction affects language interference in the third language

   https://doi.org/10.1017/S1366728921000043  

   Tomoschuk, Brendan; Duyck, Wouter; Hartsuiker, Robert J.; Ferreira, Victor S.; Gollan, Tamar H. (August 2021, Bilingualism: Language and Cognition)

   null (Ed.)

   Abstract Applied linguistic work claims that multilinguals’ non-native languages interfere with one another based on similarities in cognitive factors like proficiency or age of acquisition. Two experiments explored how trilinguals regulate control of native- and non-native-language words. Experiment 1 tested 46 Dutch–English–French trilinguals in a monitoring task. Participants decided if phonemes were present in the target language name of a picture, phonemes of non-target language translations resulted in longer response times and more false alarms compared to phonemes not present in any translation (Colomé, 2001). The second language (English) interfered more than the first (Dutch) when trilinguals monitored in their third language (French). In Experiment 2, 95 bilinguals learned an artificial language to explore the possibility that the language from which a bilingual learns a third language provides practice managing known-language interference. Language of instruction modulated results, suggesting that learning conditions may reduce interference effects previously attributed to cognitive factors. 

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2. Testing the Ability of Language Models to Interpret Figurative Language

   https://doi.org/10.18653/v1/2022.naacl-main.330  

   Liu, Emmy; Cui, Chenxuan; Zheng, Kenneth; Neubig, Graham (July 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)

   Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition. However, figurative language has been a relatively under-studied area in NLP, and it remains an open question to what extent modern language models can interpret nonliteral phrases. To address this question, we introduce Fig-QA, a Winograd-style nonliteral language understanding task consisting of correctly interpreting paired figurative phrases with divergent meanings. We evaluate the performance of several state-of-the-art language models on this task, and find that although language models achieve performance significantly over chance, they still fall short of human performance, particularly in zero- or few-shot settings. This suggests that further work is needed to improve the nonliteral reasoning capabilities of language models. 

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3. The (moral) language of hate

   https://doi.org/10.1093/pnasnexus/pgad210  

   Kennedy, Brendan; Golazizian, Preni; Trager, Jackson; Atari, Mohammad; Hoover, Joe; Mostafazadeh Davani, Aida; Dehghani, Morteza; Van Bavel, ed., J. (July 2023, PNAS Nexus)

   Abstract Humans use language toward hateful ends, inciting violence and genocide, intimidating and denigrating others based on their identity. Despite efforts to better address the language of hate in the public sphere, the psychological processes involved in hateful language remain unclear. In this work, we hypothesize that morality and hate are concomitant in language. In a series of studies, we find evidence in support of this hypothesis using language from a diverse array of contexts, including the use of hateful language in propaganda to inspire genocide (Study 1), hateful slurs as they occur in large text corpora across a multitude of languages (Study 2), and hate speech on social-media platforms (Study 3). In post hoc analyses focusing on particular moral concerns, we found that the type of moral content invoked through hate speech varied by context, with Purity language prominent in hateful propaganda and online hate speech and Loyalty language invoked in hateful slurs across languages. Our findings provide a new psychological lens for understanding hateful language and points to further research into the intersection of morality and hate, with practical implications for mitigating hateful rhetoric online. 

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4. Interactivity of language: Interactivity of Language

   https://doi.org/10.1111/lnc3.12282  

   Spevack, Samuel C.; Falandays, J. Benjamin; Batzloff, Brandon; Spivey, Michael J. (July 2018, Language and Linguistics Compass)
5. Testing the limits of natural language models for predicting human language judgements

   https://doi.org/10.1038/s42256-023-00718-1  

   Golan, Tal; Siegelman, Matthew; Kriegeskorte, Nikolaus; Baldassano, Christopher (September 2023, Nature Machine Intelligence)