United States

Decision support systems based on clinical notes have the potential to improve patient care by pointing doctors towards overseen risks. Predicting a patient&#39;s outcome is an essential part of such systems, for which the use of deep neural networks has shown promising results. However, the patterns learned by these networks are mostly opaque and previous work revealed both reproduction of systemic biases and unexpected behavior for out-of-distribution patients. For application in clinical practice it is crucial to be aware of such behavior. We thus introduce a testing framework that evaluates clinical models regarding certain changes in the input. The framework helps to understand learned patterns and their influence on model decisions. In this work, we apply it to analyse the change in behavior with regard to the patient characteristics gender, age and ethnicity. Our evaluation of three current clinical NLP models demonstrates the concrete effects of these characteristics on the models&#39; decisions. They show that model behavior varies drastically even when fine-tuned on the same data with similar AUROC score. These results exemplify the need for a broader communication of model behavior in the clinical domain.

NAACL 2022

What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

diagnosis prediction

framework

multi-label classification

clinical nlp

transformers

analysis

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Decision support systems based on clinical notes have the potential to improve patient care by pointing doctors towards overseen risks. Predicting a patient's outcome is an essential part of such systems, for which the use of deep neural networks has shown promising results. However, the patterns learned by these networks are mostly opaque and previous work revealed both reproduction of systemic biases and unexpected behavior for out-of-distribution patients. For application in clinical practice it is crucial to be aware of such behavior. We thus introduce a testing framework that evaluates clinical models regarding certain changes in the input. The framework helps to understand learned patterns and their influence on model decisions. In this work, we apply it to analyse the change in behavior with regard to the patient characteristics gender, age and ethnicity. Our evaluation of three current clinical NLP models demonstrates the concrete effects of these characteristics on the models' decisions. They show that model behavior varies drastically even when fine-tuned on the same data with similar AUROC score. These results exemplify the need for a broader communication of model behavior in the clinical domain.

workshop paper

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. We analyze this dataset to characterize the types of information sought by medical experts. We also train baseline models for trigger detection and question generation (QG), paired with unsupervised answer retrieval over EHRs. Our baseline model is able to generate high quality questions in over 62% of cases when prompted with human selected triggers. We release this dataset (and all code to reproduce baseline model results) to facilitate further research into realistic clinical QA and QG.

Learning to Ask Like a Physician

Although there are many clinical and biomedical language models for the English language, there is still a significant lack of models for Spanish. In this presentation, we introduce Clinical Flair, the first Spanish language model trained on real diagnoses from the Chilean public healthcare system. Taking the Named Entity Recognition task as a case study, we show that contextualized embeddings retrieved from our domain-specific language model outperform the results of the general domain model by a wide margin. In addition, we show that training a language model on real diagnoses may be more beneficial than training on electronic health records.

Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language Processing

Recent studies show that neural natural processing models for medical code prediction suffer from a label imbalance issue. This study aims to investigate further imbalance in a medical code prediction dataset in terms of demographic variables and analyse performance differences in demographic groups. We use sample-based metrics to correctly evaluate the performance in terms of the data subject. Also, a simple label distance metric is proposed to quantify the difference in the label distribution between a group and the entire data. Our analysis results reveal that the model performs differently towards different demographic groups: significant differences between age groups and between insurance types are observed. Interestingly, we found a weak positive correlation between the number of training data of the group and the performance of the group. However, a strong negative correlation between the label distance of the group and the performance of the group is observed. This result suggests that the model tends to perform poorly in the group whose label distribution is different from the global label distribution of the training data set. Further analysis of the model performance is required to identify the cause of these differences and to improve the model building.

An exploratory data analysis: the performance differences of a medical code prediction system on different demographic groups

Sequence-to-sequence models are appealing because they allow both encoder and decoder to be shared across many tasks by formulating those tasks as text-to-text problems. Despite recently reported successes of such models, we find that engineering input/output representations for such text-to-text models is challenging. On the Clinical TempEval 2016 relation extraction task, the most natural choice of output representations, where relations are spelled out in simple predicate logic statements, did not lead to good performance. We explore a variety of input/output representations, with the most successful prompting one event at a time, and achieving results competitive with standard pairwise temporal relation extraction systems.

Exploring Text Representations for Generative Temporal Relation Extraction

The lack of resources for languages in the Americas has proven to be a problem for the creation of digital systems such as machine translation, search engines, chat bots, and more. The scarceness of digital resources for a language causes a higher impact on populations where the language is spoken by millions of people. We introduce the first official large combined corpus for deep learning of an indigenous South American low-resource language spoken by millions called Quechua. Specifi-
cally, our curated corpus is created from text gathered from the southern region of Peru where a dialect of Quechua is spoken that has not traditionally been used for digital systems as a target dialect in the past. In order to make our work repeatable by others, we also offer a public, pre-trained, BERT model called QuBERT which is the largest linguistic model ever trained for any Quechua type, not just the south-
ern region dialect. We furthermore test our corpus and its corresponding BERT model on two major tasks: (1) named-entity recognition (NER) and (2) part-of-speech (POS) tagging by using state-of-the-art techniques where we achieve results comparable to other work on higher-resource languages. In this article, we
describe the methodology, challenges, and results from the creation of QuBERT which is on on par with other state-of-the-art multilingual models for natural language processing achieving between 71 and 74% F1 score on NER and
84–87% on POS tasks.

Introducing QuBERT: A Large Monolingual Corpus and BERT Model for Southern Quechua

The distant supervision (DS) paradigm has been widely used for relation extraction (RE) to alleviate the need for expensive annotations. However, it suffers from noisy labels, which leads to worse performance than models trained on human-annotated data, even when trained using hundreds of times more data. We present a systematic study on the use of natural lan- guage inference (NLI) to improve distantly su- pervised document-level RE. We apply NLI in three scenarios: (i) as a filter for denoising DS labels, (ii) as a filter for model prediction, and (iii) as a standalone RE model. Our re- sults show that NLI filtering consistently im- proves performance, reducing the performance gap with a model trained on human-annotated data by 2.3 F1.

Improving Distantly Supervised Document-Level Relation Extraction Through Natural Language Inference

Training a tagger for Named Entity Recognition (NER) requires a substantial amount of labeled data in the task domain.
Manual labeling is a tedious and complicated task.
Semi-supervised learning methods can reduce the quantity of labeled data necessary to train a model.
However, these methods require large quantities of unlabeled data, which remains an issue in many cases.


We address this problem by generating unlabeled data.
Large language models have proven to be powerful tools for text generation.
We use their generative capacity to produce new sentences and variations of the sentences of our available data.
This generation method, combined with a semi-supervised method, is evaluated on CoNLL and I2B2.
We prepare both of these corpora to simulate a low resource setting.
We obtain significant improvements for semi-supervised learning with synthetic data against supervised learning on natural data.


Generating unlabelled data for a tri-training approach in a low resourced NER task

Text segmentation and extraction from unstructured documents can provide business researchers with a wealth of new information on firms and their behaviors. However, the most valuable text is often difficult to extract consistently due to substantial variations in how content can appear from document to document. Thus, the most successful way to extract this content has been through costly crowdsourcing and training of manual workers. We propose the Assisted Neural Text Segmentation (ANTS) framework to identify pertinent text in unstructured documents from a small set of labeled examples. ANTS leverages deep learning and transfer learning architectures to empower researchers to identify relevant text with minimal manual coding. Using a real world sample of accounting documents, we identify targeted sections 96% of the time using only 5 training examples.

ANTS: A Framework for Retrieval of Text Segments in Unstructured Documents

Back translation is one of the most widely used methods for improving the performance of neural machine translation systems. Recent research has sought to enhance the effectiveness of this method by increasing the ‘diversity’ of the generated translations. We argue that the definitions and metrics used to quantify
‘diversity’ in previous work have been insufficient. This work puts forward a more nuanced framework for understanding diversity in training data, splitting it into lexical diversity and syntactic diversity. We present novel metrics for measuring these different aspects of diversity and carry out empirical analysis into
the effect of these types of diversity on final neural machine translation model performance for low-resource English to/from Turkish and mid-resource English to/fromIcelandic. Our findings show that generating back translation using nuc-
leus sampling results in higher final model performance, and that this method of generation has high levels of both lexical and syntactic diversity. We also find evidence that lexical diversity is more important than syntactic for back translation performance.

Exploring diversity in back translation for low-resource machine translation

Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability. In addition, building a punctuation restoration system is challenging for low-resource languages, especially for domain-specific applications. In this paper, we propose a Spanish punctuation restoration system designed for a real-time customer support transcription service. To address the data sparsity of Spanish transcripts in the customer support domain, we introduce two transfer-learning-based strategies: 1) domain adaptation using out-of-domain Spanish text data; 2) cross-lingual transfer learning leveraging in-domain English transcript data. Our experiment results show that these strategies improve the accuracy of the Spanish punctuation restoration system.

What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

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What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

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Learning to Ask Like a Physician

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The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models

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