Mexico

Modern natural language processing (NLP) techniques increasingly require substantial amounts of data to train robust algorithms. Building such technologies for low-resource languages requires focusing on data creation efforts and data-efficient algorithms. For a large number of low-resource languages, especially Indigenous languages of the Americas, this data exists in image-based non-machine-readable documents. This includes scanned copies of comprehensive dictionaries, linguistic field notes, children&#39;s stories, and other textual material. To digitize these resources, Optical Character Recognition (OCR) has played a major role but it comes with certain challenges in low-resource settings. In this paper, we share the first survey of OCR techniques specific to low-resource data creation settings and outline several open challenges, with a special focus on Indigenous Languages of the Americas. Based on experiences and results from previous research, we conclude with recommendations on utilizing and improving OCR for the benefit of computational researchers, linguists, and language communities.

NAACL 2024

A Concise Survey of OCR for Low-Resource Languages

optical character recognition

low-resource

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 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 will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
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ACL 2022

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 (NLP). 

Modern natural language processing (NLP) techniques increasingly require substantial amounts of data to train robust algorithms. Building such technologies for low-resource languages requires focusing on data creation efforts and data-efficient algorithms. For a large number of low-resource languages, especially Indigenous languages of the Americas, this data exists in image-based non-machine-readable documents. This includes scanned copies of comprehensive dictionaries, linguistic field notes, children's stories, and other textual material. To digitize these resources, Optical Character Recognition (OCR) has played a major role but it comes with certain challenges in low-resource settings. In this paper, we share the first survey of OCR techniques specific to low-resource data creation settings and outline several open challenges, with a special focus on Indigenous Languages of the Americas. Based on experiences and results from previous research, we conclude with recommendations on utilizing and improving OCR for the benefit of computational researchers, linguists, and language communities.

workshop paper

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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Please register!

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

The current focus on resource-rich languages poses a challenge to linguistic diversity, affecting minority languages with limited digital presence and relatively old published and unpublished resources. In addressing this issue, this study targets the digitalization of old scanned textbooks written in four Peruvian indigenous languages (Asháninka, Shipibo-Konibo, Yanesha, and Yine) using Optical Character Recognition (OCR) technology. This is complemented with text correction methods to minimize extraction errors. Contributions include the creation of an annotated dataset with 454 scanned page images, for a rigorous evaluation, and the development of a module to correct OCR-generated transcription alignments.

Unlocking Knowledge with OCR-Driven Document Digitization for Peruvian Indigenous Languages

This article presents an ongoing research on one of the several native languages of the Americas: Amuzgo or jny'on3 nda3 . This language is spoken in Southern Mexico and belongs to the Otomanguean family. Although Amuzgo vitality is stable and there are some available resources, such as grammars, dictionaries, or literature, its digital inclusion is emerging (cf. Eberhard et al. (2024)). In this respect, here is described the creation of a curated dataset in Amuzgo. This resource is intended to contribute the development of tools for scarce resources languages by providing fine-grained linguistic information in different layers: From data collection with native speakers to data annotation. The dataset was built according to the following method: i) data collection in Amuzgo by means of linguistic fieldwork; ii) acoustic data processing; iii) data transcription; iv) glossing and translating data into Spanish; v) semiautomatic alignment of translations; and vi) data systematization. This resource is released as an open access dataset to foster the academic community to explore the richness of this language.

From Field Linguistics to NLP: Creating a curated dataset in Amuzgo language

In this paper we describe our work on Task~2: Creation of Educational Materials. We tried three approaches, but only the third approach yielded improvement over the baseline system. The first system was a fairly generic transformer model. The second system was our own implementation of the edit tree approach from the baseline system. Our final attempt was a version of the baseline system where if no transformation succeeded, we applied transformations from similar morphosyntactic relations. We describe all three here, but, in the end, we only submitted the third system.

The role of morphosyntactic similarity in generating related sentences

Summarizing medical conversations poses unique challenges due to the specialized domain and the difficulty of collecting in-domain training data. In this study, we investigate the performance of state-of-the-art doctor-patient conversation generative summarization models on the out-of-domain data. We divide the summarization model of doctor-patient conversation into two configurations: (1) a general model, without specifying subjective (S), objective (O), and assessment (A) and plan (P) notes; (2) a SOAP-oriented model that generates a summary with SOAP sections. We analyzed the limitations and strengths of the fine-tuning language model-based methods and GPTs on both configurations. We also conducted a Linguistic Inquiry and Word Count analysis to compare the SOAP notes from different datasets. The results exhibit a strong correlation for reference notes across different datasets, indicating that format mismatch (i.e., discrepancies in word distribution) is not the main cause of performance decline on out-of-domain data. Lastly, a detailed analysis of SOAP notes is included to provide insights into missing information and hallucinations introduced by the models.

Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes

The advancement of natural language processing (NLP) systems in healthcare hinges on language models' ability to interpret the intricate information contained within clinical notes. 
This process often requires integrating information from various time points in a patient's medical history. 
However, most earlier clinical language models were pretrained with a context length limited to roughly one clinical document. 
In this study, We introduce ClinicalMamba, a specialized version of the Mamba language model, pretrained on a vast corpus of longitudinal clinical notes to address the unique linguistic characteristics and information processing needs of the medical domain. 
ClinicalMamba models, with 130 million and 2.8 billion parameters, demonstrate superior performance in modeling clinical language across extended text lengths compared to Mamba and other clinical models based on longformer and Llama. 
With few-shot learning, ClinicalMamba achieves notable benchmarks in speed and performance, outperforming existing clinical language models and large language models like GPT-4 in longitudinal clinical tasks.

ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes

Automatic depression detection from conversational data has gained significant interest in recent years.
The DAIC-WOZ dataset, interviews conducted by a human-controlled virtual agent, has been widely used for this task.
Recent studies have reported enhanced performance when incorporating interviewer's prompts into the model.
In this work, we hypothesize that this improvement might be mainly due to a bias present in these prompts, rather than the proposed architectures and methods.
Through ablation experiments and qualitative analysis, we discover that models using interviewer's prompts learn to focus on a specific region of the interviews, where questions about past experiences with mental health issues are asked, and use them as discriminative shortcuts to detect depressed participants. 
In contrast, models using participant responses gather evidence from across the entire interview.
Finally, to highlight the magnitude of this bias, we achieve a 0.90 F1 score by intentionally exploiting it, the highest result reported to date on this dataset using only textual information.
Our findings underline the need for caution when incorporating interviewers' prompts into models, as they may inadvertently learn to exploit targeted prompts, rather than learning to characterize the language and behavior that are genuinely indicative of the patient's mental health condition.

DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviews

Information extraction from Electronic Health Records (EHRs) is a crucial task in healthcare, and the lack of resources and language specificity pose significant challenges. This study addresses the limited availability of Italian Natural Language Processing (NLP) tools for clinical applications and the computational demand of large language models (LLMs) for training. We present LlamaMTS, an instruction-tuned Llama for the Italian language, leveraging the LoRA technique. It is ensembled with a BERT-based model to classify EHRs based on the presence or absence of metastasis in patients affected by Breast cancer. Through our evaluation analysis, we discovered that LlamaMTS exhibits superior performance compared to both zero-shot LLMs and other Italian BERT-based models specifically fine-tuned on the same metastatic task. LlamaMTS demonstrates promising results in resource-constrained environments, offering a practical solution for information extraction from Italian EHRs in oncology, potentially improving patient care and outcomes.

LlamaMTS: Optimizing Metastasis Detection with Llama Instruction Tuning and BERT-Based Ensemble in Italian Clinical Reports

This study evaluates the proficiency of Large Language Models (LLMs) in accurately labeling clinical document excerpts. Our focus is on the assignment of potential or confirmed diagnoses and medical procedures to snippets of medical text sourced from unstructured clinical patient records. We explore how the performance of LLMs compare against human annotators in classifying these excerpts. Employing a few-shot, chain-of-thought prompting approach with the MIMIC-III dataset, Med-PaLM 2 showcases annotation accuracy comparable to human annotators, achieving a notable precision rate of approximately 92\% relative to the gold standard labels established by human experts.

Large Language Models Provide Human-Level Medical Text Snippet Labeling

Given the increasing demand for mental health assistance, artificial intelligence (AI), particularly large language models (LLMs), may be valuable for integration into automated clinical support systems. In this work, we leverage a decision transformer architecture for topic recommendation in counseling conversations between patients and mental health professionals. The architecture is utilized for offline reinforcement learning, and we extract states (dialogue turn embeddings), actions (conversation topics), and rewards (scores measuring the alignment between patient and therapist) from previous turns within a conversation to train a decision transformer model. We demonstrate an improvement over baseline reinforcement learning methods, and propose a novel system of utilizing our model's output as synthetic labels for fine-tuning a large language model for the same task. Although our implementation based on LLaMA-2 7B has mixed results, future work can undoubtedly build on the design.

Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models

Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years. Recognizing cognitive distortions from the interviewee's utterances can be an essential part of psychotherapy, especially for cognitive behavioral therapy. In this paper, we propose ERD, which improves LLM-based cognitive distortion classification performance with the aid of additional modules of (1) extracting the parts related to cognitive distortion, and (2) debating the reasoning steps by multiple agents. Our experimental results on a public dataset show that ERD improves the multi-class F1 score as well as binary specificity score. Regarding the latter score, it turns out that our method is effective in debiasing the baseline method which has high false positive rate, especially when the summary of multi-agent debate is provided to LLMs.

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