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| <?xml version='1.0' encoding='UTF-8'?> | ||
| <collection id="2024.clasp"> | ||
| <volume id="1" ingest-date="2024-10-09" type="proceedings"> | ||
| <meta> | ||
| <booktitle>Proceedings of the 2024 CLASP Conference on Multimodality and Interaction in Language Learning</booktitle> | ||
| <editor><first>Amy</first><last>Qiu</last></editor> | ||
| <editor><first>Bill</first><last>Noble</last></editor> | ||
| <editor><first>David</first><last>Pagmar</last></editor> | ||
| <editor><first>Vladislav</first><last>Maraev</last></editor> | ||
| <editor><first>Nikolai</first><last>Ilinykh</last></editor> | ||
| <publisher>Association for Computational Linguistics</publisher> | ||
| <address>Gothenburg, Sweden</address> | ||
| <month>October</month> | ||
| <year>2024</year> | ||
| <url hash="1d5530c2">2024.clasp-1</url> | ||
| <venue>clasp</venue> | ||
| </meta> | ||
| <frontmatter> | ||
| <url hash="6bb3b9ca">2024.clasp-1.0</url> | ||
| <bibkey>clasp-2024-1</bibkey> | ||
| </frontmatter> | ||
| <paper id="1"> | ||
| <title>Critical Size Hypothesis: How Model Hyperparameters Correlate with Its Linguistic Abilities</title> | ||
| <author><first>Ekaterina</first><last>Voloshina</last></author> | ||
| <author><first>Oleg</first><last>Serikov</last></author> | ||
| <pages>1–7</pages> | ||
| <abstract>In recent years, the models were tested on different probing tasks to examine their language knowledge. However, few researchers explored the very process of models’ language acquisition. Nevertheless, the analysis of language acquisition during training could shed light on the model parameters that help to acquire the language faster. In this work, we experiment with model hyperparameters and reveal that the hidden size is the most essential factor for model language acquisition.</abstract> | ||
| <url hash="cb596c62">2024.clasp-1.1</url> | ||
| <bibkey>voloshina-serikov-2024-critical</bibkey> | ||
| </paper> | ||
| <paper id="2"> | ||
| <title><fixed-case>INIKOL</fixed-case> - Collocational Database for Learning <fixed-case>C</fixed-case>roatian as a Foreign Language</title> | ||
| <author><first>Goranka</first><last>Blagus Bartolec</last></author> | ||
| <author><first>Gorana</first><last>Duplančić Rogošić</last></author> | ||
| <author><first>Antonia</first><last>Ordulj</last></author> | ||
| <pages>8–12</pages> | ||
| <abstract>This paper describes the ongoing work on the INIKOL project - the development of a collocation database for learning Croatian as a foreign language. The main goal of the project is to contribute to easier mastery of collocations as fixed phrases in Croatian as a foreign language.</abstract> | ||
| <url hash="a902be3c">2024.clasp-1.2</url> | ||
| <bibkey>blagus-bartolec-etal-2024-inikol</bibkey> | ||
| </paper> | ||
| <paper id="3"> | ||
| <title>How Does an Adjective Sound Like? Exploring Audio Phrase Composition with Textual Embeddings</title> | ||
| <author><first>Saba</first><last>Nazir</last></author> | ||
| <author><first>Mehrnoosh</first><last>Sadrzadeh</last></author> | ||
| <pages>13–18</pages> | ||
| <abstract>We learn matrix representations for the fre- quent sound-relevant adjectives of English and compose them with vector representations of their nouns. The matrices are learnt jointly from audio and textual data, via linear regres- sion and tensor skipgram. They are assessed using an adjective similarity benchmark and also a novel adjective-noun phrase similarity dataset, applied to two tasks: semantic similar- ity and audio similarity. Joint learning via Ten- sor Skipgram (TSG) outperforms audio-only models, matrix composition outperforms addi- tion and non compositional phrase vectors.</abstract> | ||
| <url hash="e2728db2">2024.clasp-1.3</url> | ||
| <bibkey>nazir-sadrzadeh-2024-adjective</bibkey> | ||
| </paper> | ||
| <paper id="4"> | ||
| <title>Learning through gesture: embodied repetitions in tandem interactions</title> | ||
| <author><first>Loulou</first><last>Kosmala</last></author> | ||
| <pages>19–25</pages> | ||
| <abstract>Grounded in an interactional approach, this corpus-based study presents an analysis of multimodal tandem interactions held in English between tandem partners (L1 and L2 speakers) to study other-repetitions across different levels and modalities. In particular, I investigate cases of embodied repetitions in contexts of co-construction and repair whereby tandem partners negotiate meaning. Based on careful micro-analyses of data fragments, analyses reveal different types of temporal coordination between the repetition of the target item and/or of the gesture, addressing specific issues at different linguistic levels. While repetitions typically occur in linguistic-oriented contexts, emerging gestures may further contribute to mutual understanding and alignment.</abstract> | ||
| <url hash="74b9afac">2024.clasp-1.4</url> | ||
| <bibkey>kosmala-2024-learning</bibkey> | ||
| </paper> | ||
| <paper id="5"> | ||
| <title>Towards Automated Game-Based Early Screening for Language Disorder</title> | ||
| <author><first>Hamdan Hamid</first><last>Al-Ali</last></author> | ||
| <author><first>Elsa</first><last>Soares</last></author> | ||
| <author><first>Goncalo</first><last>Leal</last></author> | ||
| <author><first>Rita</first><last>Valente</last></author> | ||
| <author><first>Nicole</first><last>Agrela</last></author> | ||
| <author><first>Alexandra</first><last>Marquis</last></author> | ||
| <author><first>Hanan</first><last>Aldarmaki</last></author> | ||
| <pages>26–31</pages> | ||
| <abstract>This paper examines the potential of gamifying early childhood language disorder screening to make the process more accessible and scalable. We provide an overview of current practices in screening and assessment, and a description of our on-going work towards automation of early screening. By integrating developmental milestones into a video game format and employing automatic speech recognition and natural language processing, this approach aims to enhance the efficiency and reach of early screening in order to identify children who need further professional assessment.</abstract> | ||
| <url hash="0595a602">2024.clasp-1.5</url> | ||
| <bibkey>al-ali-etal-2024-towards</bibkey> | ||
| </paper> | ||
| <paper id="6"> | ||
| <title><fixed-case>L</fixed-case>2 Interaction in Heterogeneous Learner Groups during Content and Language Integrated Learning: The Experience of (removed for peer-review) and beyond</title> | ||
| <author><first>Julia</first><last>Edeleva</last></author> | ||
| <author><first>Martin</first><last>Neef</last></author> | ||
| <author><first>Jiaming</first><last>Liu</last></author> | ||
| <author><first>Martin</first><last>Scheidt</last></author> | ||
| <pages>32–38</pages> | ||
| <abstract>Сontent and language integrated learning is considered a powerful tool to promote inclusion in educational settings of learners for whom the language of instruction is their additional language. Language-related difficulties of those learners have been claimed detrimental for attaining personal educational goals. Academic language places increased cognitive demands on the learning process in general due to 1) its internal complexity; 2) L2 speakers’ lower proficiency; 3) their disadvantage in terms of real-time processing. Facilitators are, therefore, encouraged to integrate interactional CLIL-elements (e.g., scaffolding) during content instruction that provide the necessary pedagogical support for better understanding of disciplinary concepts and their interrelation. In the current contribution, we present the concept and first results of Rail.lexis, a collaborative project of the Department of German Studies and the Department of Railway Engineering at TU Brauschweig. We present and discuss several conversational arrangements (e.g., word guessing games, a differential task matrix) that were designed to engage the learners of heterogeneous linguistic backgrounds in meaningful interactions in subject-specific classes. Subject-specific tasks are gradient regarding their cognitive complexity and the background knowledge required to solve them. Therefore, the linguistic repertoire required to negotiate different task types is also differential to ensure the participation of linguistically diverse students in language-enhanced classroom interactions.</abstract> | ||
| <url hash="57fed3c7">2024.clasp-1.6</url> | ||
| <bibkey>edeleva-etal-2024-l2</bibkey> | ||
| </paper> | ||
| <paper id="7"> | ||
| <title>Fifty shapes of <fixed-case>BL</fixed-case>i<fixed-case>MP</fixed-case>: syntactic learning curves in language models are not uniform, but sometimes unruly</title> | ||
| <author><first>Bastian</first><last>Bunzeck</last></author> | ||
| <author><first>Sina</first><last>Zarrieß</last></author> | ||
| <pages>39–55</pages> | ||
| <abstract>Syntactic learning curves in LMs are usually reported as relatively stable and power law-shaped. By analyzing the learning curves of different LMs on various syntactic phenomena using both small self-trained llama models and larger pre-trained pythia models, we show that while many phenomena do follow typical power law curves, others exhibit S-shaped, U-shaped, or erratic patterns. Certain syntactic paradigms remain challenging even for large models, resulting in persistent preference for ungrammatical sentences. Most phenomena show similar curves for their paradigms, but the existence of diverging patterns and oscillations indicates that average curves mask important developments, underscoring the need for more detailed analyses of individual learning trajectories.</abstract> | ||
| <url hash="def43411">2024.clasp-1.7</url> | ||
| <bibkey>bunzeck-zarriess-2024-fifty</bibkey> | ||
| </paper> | ||
| <paper id="8"> | ||
| <title>Not Just Semantics: Word Meaning Negotiation in Social Media and Spoken Interaction</title> | ||
| <author><first>Staffan</first><last>Larsson</last></author> | ||
| <author><first>Jenny</first><last>Myrendal</last></author> | ||
| <author><first>Bill</first><last>Noble</last></author> | ||
| <pages>56–61</pages> | ||
| <abstract>This paper outlines the ongoing research project “Not Just Semantics: Word Meaning Negotiation in Social Media and Spoken Interaction”. The goal of the project is to investigate how meanings of words (and phrases) are interactively negotiated in social media and in spoken interaction. This project will contribute towards a comprehensive theory of word meaning negotiation.</abstract> | ||
| <url hash="005731a5">2024.clasp-1.8</url> | ||
| <bibkey>larsson-etal-2024-just</bibkey> | ||
| </paper> | ||
| <paper id="9"> | ||
| <title>Toward Real Time Word Based Prosody Recognition</title> | ||
| <author><first>Alex</first><last>Tilson</last></author> | ||
| <author><first>Frank</first><last>Foerster</last></author> | ||
| <pages>62–67</pages> | ||
| <abstract>Prosodic salience is a heuristic based on word-level prosody in child-directed speech that is thought to serve as a cue for attentional focus. It has been used in the context of robotic language acquisition to extract the contextually most relevant words from a human tutor’s speech to ground them in a robot’s sensorimotor data. However, the pipeline for performing word-based prosody-recognition operated in a semi-automatic manner and required substantial manual effort. We describe our efforts to automate the existing pipeline by including real time prosody recognition, and a modern speech recognition and forced alignment model. The intention is to enable its use in real time for human-in-the-loop robotic language acquisition and other socially driven forms of online learning.</abstract> | ||
| <url hash="d7cad9cf">2024.clasp-1.9</url> | ||
| <bibkey>tilson-foerster-2024-toward</bibkey> | ||
| </paper> | ||
| </volume> | ||
| </collection> |