Seminars

We hold regular online or hybrid research seminars (as invited talks or reading groups). Our seminars are supported by IDSAI (Institute of Data Science and Artificial Intelligence) at the University of Exeter and NLP group from the University of Sheffield. 

All times below are BST time (London). We update seminar information (with a Zoom link) through Google Group.

Upcoming seminars:

12 Dec 2024, 4-5pm: Dr Vered Shwartz at the University of British Columbia

• Title: Navigating Cultural Adaptation of LLMs: Knowledge, Context, and Consistency

• Abstract: Despite their amazing success, large language models and vision and language models suffer from several limitations. This talk focuses on one of these limitations: the models’ narrow Western, North American, or even US-centric lens, as a result of training on web text and images primarily from US-based users. As a result, users from diverse cultures that are interacting with these tools may feel misunderstood and experience them as less useful. Worse still, when such models are used in applications that make decisions about people’s lives, lack of cultural awareness may lead to models perpetuating stereotypes and reinforcing societal inequalities. In this talk, I will present a line of work from our lab aimed at quantifying and mitigating this bias.

16 Jan 2024, 3-4pm: Prof Roberto Navigli at Sapienza University of Rome

• Title: What's Behind Text? The Long, Challenging Path Towards a Unified Language-Independent Representation of Meaning

• Abstract: In the era of Large Language Models (LLMs), the pursuit of a unified, language-independent representation of meaning remains both essential and complex. This talk revisits the rationale for advancing semantic understanding beyond the capabilities of LLMs and highlights the development of a large-scale multilingual inter-task resource like MOSAICo and the design of innovative methods that bridge word- and sentence-level meanings across languages. I will also explore how building a robust, multilingual framework for interpreting meaning with greater precision and depth enhances the quality and reliability of system outputs, including text generated by LLMs.

Previous seminars:

24 Oct 2024, 3-4pm: Rosni Vasu at the University of Zurich

• Title: SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles

• Abstract: Scientific discovery involves understanding and structuring hypotheses, a challenging task due to the complexity of scientific texts. This talk presents SciHyp, a novel dataset containing RDF descriptions of 689 hypothesis sentences from 479 computer science articles. These hypotheses include relation-finding and comparative types. The dataset was created using a multi-step annotation pipeline with expert annotation, Language Models (LMs) like BERT and Sci-BERT, and crowd-based refinement. Our pipeline effectively identified non-hypothesis sentences with a 96.1% consensus rate between the LMs and crowd annotations, demonstrating its effectiveness in identifying relevant sentences that contain hypotheses. We also used GPT-4 to extract hypothesis components. SciHyp aims to benefit the scientific community by providing a structured dataset for model training and evaluation. The talk concludes with a glimpse into an ongoing project making use of the SciHyp pipeline for scientific hypotheses generation.

• Link to video recording (Passcode: Ru7p.EQ?)

10 Oct 2024, 3-4pm: Dr Rodrigo Souza Wilkens at the University of Exeter and previously at Université catholique de Louvain

• Title: Assessing Language Proficiency: What Are We Really Measuring with NLP?

• Abstract: The use of computational models in assessing language proficiency has grown significantly, yet questions remain about which aspects of language these models can effectively encode. This presentation assesses the capabilities of Large Language Models (LLMs) in the context of language proficiency assessment in the French language, focusing on their application to Automatic Readability Assessment and Automated Essay Scoring. The presentation will address how effectively LLMs capture linguistic skills (e.g., lexical and syntactic knowledge) and assess the potential of combining traditional linguistic features with transformer-based models to enhance the model representation. The presentation will conclude by discussing the alignment between linguistic theory and computational approaches and highlighting both current achievements and ongoing challenges.

• Link to slides and video recording (Passcode: 8.@1GgYp)

17 May 2024, 2-3pm: Dr Filip Miletić at the University of Stuttgart. 

• Title: Modeling the compositionality of noun compounds: Challenges in models and data

• Abstract: The meaning of multiword expressions such as noun compounds is characterized by varying degrees of compositionality, i.e. it may be similar to the sum of the component parts (e.g. climate change) or unrelated to it (e.g. silver bullet). This is one key feature that makes computational modeling of compound semantics remarkably challenging. In the first part of the talk, I will present recent work on probing pretrained language models for noun compound semantics, which highlights strong variability in performance with respect to properties of model architectures as well as preprocessing decisions. In the second part of the talk, I will discuss research aiming to model the evolution of compound meanings over time, comparing modeling approaches of various complexity while working on diachronic corpora as a notoriously sparse type of data.

• Link to slides and video recording (Passcode: c4PWH?sK)

25 April 2024, 2-3pm: Dr Hang Dong at the University of Exeter.

• Title: Integrating Language Models and Knowledge Graphs for NLP in Healthcare

• Abstract: Neural Language Models and Knowledge Graphs are two distinct ways in AI with a broadly similar aim to organise data and information for their retrieval. They can be integrated to provide more explainable and trustworthy AI applications. I will present previous research on classifying and linking texts to Knowledge Graphs (including ontologies) in healthcare, including the applications of automated clinical coding, rare disease identification, and discovering new medical concepts to enrich a Knowledge Graph. Finally, I will present a research agenda on integrating Large Language Models and Knowledge Graphs to process heterogeneous data to support healthcare professionals (e.g., in dementia).

• Link to slides and video recording (Passcode: h&0m7QcE)

12 April 2024, 2-3pm: Dr Marten van Schijndel at Cornell University.

• Title: Next Best Bytes Belie Robust Representations

• Abstract: In language applications, vectors from neural network language models are commonly used to encode semantic information. While these proxies work well for natural language processing tasks, it remains an open question how human-like the resulting representations are. First, I will argue that language model training objectives produce language representations that differ in key ways from human linguistic representations. I will then discuss multiple studies from my group that identify weaknesses in standard tokenization and analysis methods which produce mismatches with human linguistic response patterns, after which I will suggest alternatives to better model human responses.

• Link to video recording (Passcode: ZVBx%T$7)

2 April 2024, 3-4pm: Prof Lilian Cristine Hübne at Pontifical Catholic University of Rio Grande do Sul - PUCRS - Brazil. 

• • Title: Language and cognitive assessment in (a)typical adulthood and aging using computational tools

  • Abstract: Studies have demonstrated the influence of aging on language and general cognitive processing. Variables such as educational level and literacy habits over the course of a lifetime can be associated with cognitive-linguistic decline, maintenance, or improvement in healthy older adults and also appear to play a role in the onset of cognitive decline and dementia syndromes. This presentation will discuss studies that have been developed in language and cognitive assessment using computational tools and how the integration of these fields can contribute to the diagnosis of cognitive disorders as well as to the differentiation of groups by socioeconomic and cultural characteristics in adulthood and aging.

  • Link to slides and video recording (Passcode: #A6#qRWG)