ARC DECRA Fellow | Lecturer @cis_unimelb Technology for Under-resourced Languages SIGTYP @sig_typ Dr 🐱 Mum of Mark & Lea
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ALT Top: The title, authors, and journal of the paper. Title: "Reconciling symbols and neural networks: Evidence for a quasi-symbolic view." Authors: "Tom McCoy, Abi Tenenbaum, Zhang Enyan, Zhenghao Herbert Zhou." Journal: "Current Opinion in Behavioral Sciences" Bottom: A figure displaying how the vector representations learned by neural networks often show emergent symbolic structure. For instance, they can often be decomposed into meaningful constituents (illustrated in the figure by adding constituents to make a vector) or classified in vector space along symbolic dimensions (shown by an image of linear classifiers). However, these vectors are only quasi-symbolic: they deviate from pure symbolic structure in important ways - e.g., they can display graded similarity (“movie” is closer to “novel” than to “meal”), and they may capture graded context effects such as the word “good” affecting different nouns differently (the image shows such graded similarity among points in space
ALT For our submission to the WMT26 Creole Language Translation Shared Task, we focus on machine translation (MT) models for Pacific creoles: Tok Pisin, Bislama, and Solomon Pijin, with particular attention to broad domain performance. After pre-training on a large collection of domain-imbalanced data, we continue fine-tuning on a diverse mix of domain-balanced data. We rely on a number of data collection and preparation techniques, including LLM-assisted respelling and alignment, back-translation, and distillation from Gemini for domains originally not present in training data. Evaluated on Bouquet and a novel test set made of spoken language transcripts, our models beat open model baselines by 3+ chrF++ points in all directions with human-original references. Looking ahead, we plan to develop human-translated test sets for Solomon Pijin and Bislama, and to distil our best models into much smaller ones that retain broad domain coverage.