Venue: Lecture Theatre 7, Diamond Building 

Note: all research talks will be delivered in-person. There will be no facility to join online.

A number of submissions are also in review at international conferences. As a result, their titles and abstracts have been redacted to ensure they conform with the requirements for double blind review. The titles and abstracts will be published shortly before the SLT CDT Annual Conference.

Talk 1 Redacted

Authors: Hend ElGhazaly (University of Sheffield), Nafise Sadat Moosavi (University of Sheffield), Heidi Christensen (University of Sheffield)

Abstract: Redacted

Talk 2 Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

Authors: Atsuki Yamaguchi (University of Sheffield), Terufumi Morishita (Hitachi, Ltd.), Aline Villavicencio (University of Sheffield), Nikolaos Aletras (University of Sheffield)

Abstract: Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the reliance on costly specialized target language labeled data and catastrophic forgetting during adaptation. We tackle this challenge under a realistic, low-resource constraint: adapting instruct LLMs using only unlabeled target language data. We introduce Source-Shielded Updates (SSU), a selective parameter update strategy that proactively preserves source knowledge. Using a small set of source data and a parameter importance scoring method, SSU identifies parameters critical to maintaining source abilities. It then applies a column-wise freezing strategy to protect these parameters before adaptation. Experiments across five typologically diverse languages and 7B and 13B models demonstrate that SSU successfully mitigates catastrophic forgetting. It reduces performance degradation on monolingual source tasks to just 3.4% (7B) and 2.8% (13B) on average, a stark contrast to the 20.3% and 22.3% from full fine-tuning. SSU also achieves target-language performance highly competitive with full fine-tuning, outperforming it on all benchmarks for 7B models and the majority for 13B models.

Talk 3 Difference in Task Performance on Sparse Speech Representations

Authors: Wenjie Peng (University of Sheffield), Chen Chen (University of Sheffield), Thomas Hain (University of Sheffield)

Abstract: Learning speech representations that are useful for a variety of downstream tasks has received considerable attention, due to the outstanding properties of Self-Supervised Learning (SSL) trained models. Despite advancements in modelling methods, understanding the difference in task performance on representations is limited. Mainly motivated by the no-free-lunch theorem and speech production, this work investigates changes in task performance in sparse speech representations, providing interpretability analysis under the Information Bottleneck (IB) framework. Autoencoders with varying sparsity levels were trained using three SSL features, and evaluated on six tasks of SUPERB: Speech Enhancement (SE), Speaker Identification (SID), Speech Emotion Recognition (SER), Phone Recognition (PR), Automatic Speech Recognition (ASR) and Slot Filling (SF). Experiments show that: 1) different tasks manifest different degrees of sensitivity to the sparsity levels; 2) the optimal sparsity level for task performance varies; 3) the choice of SSL features has a limited impact on most tasks but with an exception of PR; 4) overall PR and ASR require more preservation of relevant information about the labels, while SID and SER demand more compression of irrelevant information, where the input quality can shift this trade-off to some degree. These findings can contribute to the design of a universal sparse speech representation learner.

Talk 4 Redacted

Authors: Misbah Farooq (Loughborough University, London), Varuna De Silva (Loughborough University, London), Xiyu Shi (Loughborough University, London)

Abstract: Redacted

Talk 5 Redacted

Authors: Aaron HA Fletcher (University of Sheffield), Mark Stevenson (University of Sheffield) 

Abstract: Redacted

Talk 6 Efficient Annotator Reliability Assessment with EffiARA

Authors: Owen Cook (University of Sheffield), Jake Vasilakes (University of Sheffield), Ian Roberts (University of Sheffield), Xingyi Song (University of Sheffield)

Abstract: As machine learning is becoming increasingly data-centric, we should continuously ask: “how much can we trust the data we are training and evaluating our model with?”. The models we train are a direct product of training data quality, which is itself a direct product of its annotators. The EffiARA (Efficient Annotator Reliability Assessment) framework primarily aims to understand the reliability of each individual annotator to help filter out unreliable workers or down-weight their less trustworthy labels during training. With the annotation process itself being extremely expensive and time-consuming, EffiARA also factors in cost and assists in managing annotation projects from start to finish. So far, the EffiARA framework has supported the creation of three datasets at the University of Sheffield: RUC-MCD, Chinese News Framing dataset, and SCRum-9. The EffiARA Python package is available on PyPi and open-sourced on GitHub (https://github.com/MiniEggz/EffiARA); our publicly accessible webtool is also available at https://effiara.gate.ac.uk. By accounting for annotator reliability in our dataset creation, we have observed an increase of ~5% in F1-macro for misinformation detection, and increased overall dataset reliability in the news framing task, raising the average Krippendorff's alpha from 0.396 to 0.465.