Canada

ACL 2023

Large Language Models as Cultural Technologies: Imitation and Innovation in Children and Models

[![](https://assets.underline.io/uploads/markdown_image/1/image/cbbd5f12758de05fc454b48dffd0987f.png)](https://twitter.com/QCommHub)

This Workshop aims to bring together researchers from UK’s Quantum Communication Hub programme and similar initiatives across the world to discuss technological challenges and solutions for creating the next generation of quantum communication networks featuring enhanced operational capabilities to address the requirements for a variety of applications and use cases including quantum key distribution, quantum internet, blind and distributed quantum computing.

### Our speakers and title of talks:

 **Eleni Diamanti**, Trusted and Untrusted Node QKD Networks Leveraging Telecom Technology, Pierre and Marie Curie University & Paris Centre for Quantum Computing, France
 **Sebastian Etcheverry**, CV-QKD driven by QRNG for deployment of secure communication, The Institute of Photonic Sciences (ICFO), Spain
 **Alessandro Fedrizzi,** Graph state quantum networking, Heriot-Watt University, UK
 **Hannes Hübel**, Field trial of QKD-secured Secret Sharing, Austrian Institute of Technology, Austria
 **Siddarth Koduru Joshi**, Entanglement based quantum networking, University of Bristol, UK
 **George Kanellos,** Fully Autonomous Dynamic Quantum Networking, University of Bristol, UK
 **Marc Kaplan**, Applications of quantum networks: the short and the long term, VeriQloud, France
 **Elham Kashefi**, Privacy and Security in Quantum Network, University of Edinburgh, UK and CNRS Sorbonne University, France
 **Jake Kennard**, Chip based QKD, KETS Quantum, UK
 **Navin Lingaraju**, Spectral multiplexing and information processing for quantum networks, Purdue University, USA
 **Andrew Lord,** QKD: Evolution from point-point customer solutions to national networks, BT Group, UK
 **Norbert Lütkenhaus**, QKD as performance metric for Quantum networks, University of Waterloo, Canada
 **Roger McKinlay**, *Session Chair*, Innovate UK
 **William Munro**, Architecturing robust quantum networks, NTT Basic Research Lab, Japan
 **Reza Nejabati**, *Session Chair*, University of Bristol
 **Sebastian Neumann**, QUAPITAL: In-fibre quantum key distribution crossing borders, IQOQI-Vienna, Austria
 **Richard Penty**, *Session Chair*, University of Cambridge
 **Stefano Pirandola**, Optimal Quantum Communications, University of York (Quantum Communications Hub), UK
 **John Rarity**, *Closing Remarks*, University of Bristol
 **Andrew Shields**, Extending the performance of quantum key distribution, Toshiba Europe Ltd., UK
 **Tim Spiller**, *Opening Remarks*, University of York
 **Martin Suchara**, Quantum Network Simulations - Towards Reliable, Scalable, and Secure Quantum Network Architectures, Argonne National Laboratory, USA
 **Stephanie Wehner**, Towards a Blueprint for a Quantum Internet, QuTech, TU Delft, Netherlands
 **Adrian Wonfor**, UK Quantum Networks, University of Cambridge, UK
 **Qiang Zhang**, Recent experimental progress in quantum network in China, University of Science and Technology of China, China

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Next Generation Quantum Networking Workshop 2021

keynote

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

A common metric for evaluating Automatic Speech Recognition (ASR) is Word Error Rate (WER) which solely takes into account dis- crepancies at the word-level. Although use- ful, WER is not guaranteed to correlate well with human judgment or performance on down- stream tasks that use ASR. Meaningful assess- ment of ASR mistakes becomes even more im- portant in high-stake scenarios such as health- care. We propose 2 general measures to evaluate the severity of mistakes made by ASR systems, one based on sentiment analysis and another based on text embeddings. We evaluate these measures on simulated patient-doctor conversations using 5 ASR systems. Results show that these measures capture characteristics of ASR errors that WER does not. Furthermore, we train an ASR system incorporating severity and demonstrate the potential for using severity not only in the evaluation, but in the development of ASR. Advantages and limitations of this methodology are analyzed and discussed.

Evaluating and Improving Automatic Speech Recognition using Severity

Fact-checking of health-related claims has become necessary in this digital age, where any information posted online is easily available to everyone. The most effective way to verify such claims is by using evidences obtained from reliable sources of medical knowledge, such as PubMed. Recent advances in the field of NLP have helped automate such fact-checking tasks. In this work, we propose a domain-specific BERT-based model using a transfer learning approach for the task of predicting the veracity of claim-evidence pairs for the verification of health-related facts. We also improvise on a method to combine multiple evidences retrieved for a single claim, taking into consideration conflicting evidences as well. We also show how our model can be exploited when labelled data is available and how back-translation can be used to augment data when there is data scarcity.

Multiple Evidence Combination for Fact-Checking of Health-Related Information

As opposed to general English, many concepts in biomedical terminology have been designed in recent history by biomedical professionals with the goal of being precise and concise. This is often achieved by concatenating meaningful biomedical morphemes to create new semantic units. Nevertheless, most modern biomedical language models (LMs) are pre-trained using standard domain-specific tokenizers derived from large scale biomedical corpus statistics without explicitly leveraging the agglutinating nature of biomedical language. In this work, we first find that standard open-domain and biomedical tokenizers are largely unable to segment biomedical terms into meaningful components. Therefore, we hypothesize that using a tokenizer which segments biomedical terminology more accurately would enable biomedical LMs to improve their performance on downstream biomedical NLP tasks, especially ones which involve biomedical terms directly such as named entity recognition (NER) and entity linking. Surprisingly, we find that pre-training a biomedical LM using a more accurate biomedical tokenizer does not improve the entity representation quality of a language model as measured by several intrinsic and extrinsic measures such as masked language modeling prediction (MLM) accuracy as well as NER and entity linking performance. These quantitative findings, along with a case study which explores entity representation quality more directly, suggest that the biomedical pre-training process is quite robust to instances of sub-optimal tokenization.

Biomedical Language Models are Robust to Sub-optimal Tokenization

We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or clinical text) and via discrete prompting or parameter-efficient fine-tuning. Our results consistently achieve best performance by maximally adapting to the task via pretraining on clinical text and fine-tuning on RRS examples. Importantly, this method fine-tunes a mere 0.32% of parameters throughout the model, in contrast to end-to-end fine-tuning (100% of parameters). Additionally, we study the effect of in-context examples and out-of-distribution (OOD) training before concluding with a radiologist reader study and qualitative analysis. Our findings highlight the importance of domain adaptation in RRS and provide valuable insights toward developing effective natural language processing solutions for clinical tasks.

RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

Automatically rating the quality of published
research is a critical step in medical evidence
synthesis. While several methods have been
proposed, their algorithmic fairness has been
overlooked, even though significant risks may
result when such systems are deployed in
biomedical contexts. In this work, we study
the fairness of two systems with respect to two
sensitive attributes: participant sex and med-
ical area. In some cases, we find important
inequalities, leading us to apply various debi-
asing methods. Upon examining the interplay
of predictive performance and fairness, as well
as medically-critical selective classification ca-
pabilities and calibration performance, we find
that it is possible to improve fairness through
debiasing, but often at a cost to other perfor-
mance measures.

Promoting Fairness in Classification of Quality of Medical Evidence

We consider the task of automatically extracting various overlapping frames, i.e, structured entities composed of multiple labels and mentions, from long clinical breast radiology documents. 
While many methods exist for related topics such as event extraction, slot filling, or discontinuous entity recognition, a challenge in our study resides in the fact that clinical reports typically contain overlapping frames that span multiple sentences or paragraphs.
We propose a new method that addresses these difficulties and evaluate it on a new annotated corpus.
Despite the small number of documents, we show that the hybridization between knowledge injection and a learning-based system allows us to quickly obtain proper results.
We will also introduce the concept of scope relations and show that it both improves the performance of our system, and provides a visual explanation of the predictions.


An end-to-end neural model based on cliques and scopes for frame extraction in long breast radiology reports

In this presentation, we will talk about optimal approaches to pre-train biomedical language models and evaluate their performance on the Named Entity Recognition task. Moreover, we will present our proposed method of contextualized weight distillation used to introduce domain-specific vocabulary in the continued pre-training of language models. We will also discuss how using Curriculum learning when pre-training the BIOptimus model helped us to guide the model's learning process more smoothly and introduce the biomedical language model to a broader scope of language experience.

BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition

Fine-tuning biomedical pre-trained language models (BioPLMs) such as BioBERT has become a common practice dominating leaderboards across various natural language processing tasks. Despite their success and wide adoption, prevailing fine-tuning approaches for named entity recognition (NER) naively train BioPLMs on targeted datasets without considering class distributions. This is problematic especially when dealing with imbalanced biomedical gold-standard datasets for NER in which most biomedical entities are underrepresented. In this talk, we discuss our work on addressing the class imbalance problem and propose a weighted loss trainer (WeLT), as a cost-sensitive fine-tuning approach based on new re-scaled class weights for the task of biomedical NER.


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