Canada

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pre-trained with the provider&#39;s data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language.

ACL 2023

Pre-trained language models in Spanish for health insurance coverage

insurance

pre-trained language models

clinical nlp

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pre-trained with the provider's data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language.

workshop paper

### 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.

In response to the global challenge of mental health problems, we proposes a Logical Neural Network (LNN) based Neuro-Symbolic AI method for the diagnosis of mental disorders. Due to the lack of effective therapy coverage for mental disorders, there is a need for an AI solution that can assist therapists with the diagnosis. However, current Neural Network models lack explainability and may not be trusted by therapists. The LNN is a Recurrent Neural Network architecture that combines the learning capabilities of neural networks with the reasoning capabilities of classical logic-based AI. The proposed system uses input predicates from clinical interviews to output a mental disorder class, and different predicate pruning techniques are used to achieve scalability and higher scores. In addition, we provide an insight extraction method to aid therapists with their diagnosis. The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

Medical Report Generation (MRG) is a sub-task of Natural Language Generation (NLG) and aims to present information from various sources in textual form and synthesize salient information, with the goal of reducing the time spent by domain experts in writing medical reports and providing support information for decision-making. Given the specificity of the medical domain, the evaluation of automatically generated medical reports is of paramount importance to the validity of these systems. Therefore, in this paper, we focus on the evaluation of automatically generated medical reports from the perspective of automatic and human evaluation. We present evaluation methods for general NLG evaluation and how they have been applied to domain-specific medical tasks. The study shows that MRG evaluation methods are very diverse, and that further work is needed to build shared evaluation methods. The state of the art also emphasizes that such an evaluation must be task specific and include human assessments, requesting the participation of experts in the field.

A Survey of Evaluation Methods of Generated Medical Textual Reports

In recent years, we have seen many Transformer based models being created to address Dialog Summarization problem. While there has been a lot of work on understanding how these models stack against each other in summarizing regular conversations such as the ones found in DialogSum dataset, there haven't been many analysis of these models on Clinical Dialog Summarization. In this article, we describe our solution to MEDIQA-Chat 2023 Shared Tasks as part of ACL-ClinicalNLP 2023 workshop which benchmarks some of the popular Transformer Architectures such as BioBart, Flan-T5, DialogLED, and OpenAI GPT3 on the problem of Clinical Dialog Summarization. We analyse their performance on two tasks - summarizing short conversations and long conversations. In addition to this, we also benchmark two popular summarization ensemble methods and report their performance.

HealthMavericks@MEDIQA-Chat 2023: Benchmarking different Transformer based models for Clinical Dialogue Summarization

To gain a better understanding of the linguistic information encoded in character-based language models, we probe the multilingual contextual CANINE model. We design a range of phonetic probing tasks in six Nordic languages, including Faroese as an additional zero-shot instance. We observe that some phonetic information is indeed encoded in the character representations, as consonants and vowels can be well distinguished using a linear classifier. Furthermore, results for the Danish and Norwegian language seem to be worse for the consonant/vowel distinction in comparison to other languages. The information encoded in these representations can also be learned in a zero-shot scenario, as Faroese shows a reasonably good performance in the same vowel/consonant distinction task.

The hidden folk: linguistic properties encoded in multilingual contextual character representations

Multilingual models such as mBERT have been demonstrated to exhibit impressive 
cross-lingual transfer for a number of languages. Despite this, the performance drops for lower-resourced languages, especially when they are not part of the pre-training setup and when there are script differences.
In this work we consider Maltese, a low-resource language of Arabic and Romance origins written in Latin script. Specifically, we investigate the impact of transliterating Maltese into Arabic scipt on a number of downstream tasks: Part-of-Speech Tagging, Dependency Parsing, and Sentiment Analysis. We compare multiple transliteration pipelines ranging from deterministic character maps to more sophisticated alternatives, including manually annotated word mappings and non-deterministic character mappings. For the latter, we show that selection techniques using n-gram language models of Tunisian Arabic, the dialect with the highest degree of mutual intelligibility to Maltese, yield better results on downstream tasks. Moreover, our experiments highlight that the use of an Arabic pre-trained model paired with transliteration outperforms mBERT. Overall, our results show that transliterating Maltese can be considered an option to improve the cross-lingual transfer capabilities.

Exploring the impact of transliteration on NLP performance for low-resource languages: the case of Malese and Arabic

We propose methods for transliterating English loanwords in Japanese from their Japanese written form (katakana/romaji) to their original English written form. Our data is a Japanese-English loanwords dictionary that we have created ourselves. We employ two approaches: direct transliteration, which directly converts words from katakana to English, and indirect transliteration, which utilizes the English pronunciation as a means to convert katakana words into their corresponding English sound representations, which are subsequently converted into English words. Additionally, we compare the effectiveness of using katakana versus romaji as input characters. We develop 6 models of 2 types for our experiments: one with an English lexicon-filter, and the other without. For each type, we built 3 models, including a pair n-gram based on WFSTs and two sequence-to-sequence models leveraging LSTM and transformer. Our best performing model was the pair n-gram model with a lexicon- filter, directly transliterating from katakana to English.

Back-transliteration of English loanwords in Japanese

Japanese writing is a complex system, and a large part of the complexity resides in the use of kanji. A single kanji character in modern Japanese may have multiple pronunciations, either as native vocabulary or as words borrowed from Chinese. This causes a problem for text-to-speech synthesis (TTS) because the system has to predict which pronunciation of each kanji character is appropriate in the context. The problem is called homograph disambiguation. To solve the problem, this research provides a new annotated Japanese single kanji character pronunciation data set and describes an experiment using the logistic regression (LR) classifier. A baseline is computed to compare with the LR classifier accuracy. This experiment provides the first experimental research in Japanese single kanji homograph disambiguation. The annotated Japanese data is freely released to the public to support further work.

Pronunciation ambiguities in Japanese kanji

A numeration system encodes abstract numeric quantities as concrete strings of written characters. The numeration systems used by modern scripts tend to be precise and unambiguous, but this was not so for the ancient and partially-deciphered proto-Elamite (PE) script, where written numerals can have up to four distinct readings depending on the system that is used to read them. We consider the task of disambiguating between these readings in order to determine the values of the numeric quantities recorded in this corpus. We algorithmically extract a list of possible readings for each PE numeral notation, and contribute two disambiguation techniques based on structural properties of the original documents and classifiers learned with the bootstrapping algorithm. We also contribute a test set for evaluating disambiguation techniques, as well as a novel approach to cautious rule selection for bootstrapped classifiers. Our analysis confirms existing intuitions about this script and reveals previously-unknown correlations between tablet content and numeral magnitude. This work is crucial to understanding and deciphering PE, as the corpus is heavily accounting focused and contains many more numeric tokens than tokens of text.

Disambiguating numeral sequences to decipher ancient accounting corpora

This paper presents a new approach to the ancient scripts decipherment problem based on combinatorial optimisation and coupled simulated annealing, an advanced non-convex optimisation procedure. Solutions are encoded by using k-permutations allowing for null, one-to-many, and many-to-one mappings between signs.
The proposed system is able to produce enhanced results in cognate identification when compared to the state-of-the-art systems on standard evaluation benchmarks used in literature.

Decipherment of lost ancient scripts as combinatorial optimisation using coupled simulated annealing

A crucial step in deciphering a text is to identify what set of characters were used to write it. This requires grouping character tokens according to visual and contextual features, which can be challenging for human analysts when the number of tokens or underlying types is large. Prior work has shown that this process can be automated by clustering dense representations of character images, in a task which we call “script clustering”. In this work, we present novel architectures which exploit varying degrees of contextual and visual information to learn representations for use in script clustering. We evaluate on a range of modern and ancient scripts, and find that our models produce representations which are more effective for script recovery than the current state-of-theart, despite using just 2% as many parameters. Our analysis fruitfully applies these models to assess hypotheses about the character inventory of the partially-deciphered proto-Elamite script.

Next from ACL 2023

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

Similar lecture

Development of pre-trained language models for clinical NLP in Spanish