A key contribution to being a successful researcher in natural language processing, as in any area, is having a clear overarching vision of what your body of research is trying to accomplish. Using my own 40-year career as an example, I will attempt to provide general advice on formulating and pursuing a coherent research vision. In particular, I will focus on formulating a unique, personal objective that exploits your specific talents, knowledge, and passions, and that is distinct from the current popular trends in the field. I will also focus on formulating a vision that bridges existing fields of study to produce an overarching agenda that unifies previously disparate ideas.

The Vision Thing: Finding and Pursuing your Research Passion

Transliteration is very important in the Indian
language context due to the usage of multi-
ple scripts and the widespread use of roman-
ized inputs. However, few training and evalu-
ation sets are publicly available. We introduce
Aksharantar1, the largest publicly available
transliteration dataset for Indian languages cre-
ated by mining from monolingual and paral-
lel corpora, as well as collecting data from hu-
man annotators. The dataset contains 26 mil-
lion transliteration pairs for 21 Indic lan-
guages from 3 language families using 12
scripts. Aksharantar is 21 times larger than ex-
isting datasets and is the first publicly available
dataset for 7 languages and 1 language fam-
ily. We also introduce the Aksharantar testset
comprising 103k word pairs spanning 19 lan-
guages that enables a fine-grained analysis of
transliteration models on native origin words,
foreign words, frequent words, and rare words.
Using the training set, we trained IndicXlit,
a multilingual transliteration model that im-
proves accuracy by 15% on the Dakshina test
set, and establishes strong baselines on the Ak-
sharantar testset introduced in this work. The
models, mining scripts, transliteration guide-
lines, and datasets are available at https://
github.com/AI4Bharat/IndicXlit under
open-source licenses.

Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users

Natural language serves as the primary mode of communication when an intelligent agent with a physical presence engages with human beings. While a plethora of research focuses on natural language understanding (NLU), encompassing endeavors such as sentiment analysis, intent prediction, question answering, and summarization, the scope of NLU directed at situations necessitating tangible actions by an embodied agent remains limited. The inherent ambiguity and incompleteness inherent in natural language present challenges for intelligent agents striving to decipher human intention. To tackle this predicament head-on, we introduce a novel system known as \textbf{t}ask and \textbf{a}rgument \textbf{g}rounding for \textbf{E}mbodied agents (\system). At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language. Our proposed model adopts an encoder-decoder framework enriched with nested decoding to effectively extract tasks and their corresponding arguments from these intricate instructions. These extracted tasks are then mapped (or grounded) to the robot's established collection of skills, while the arguments find grounding in objects present within the environment. To facilitate the training and evaluation of our system, we have curated a dataset featuring complex instructions. The results of our experiments underscore the prowess of our approach, as it outperforms robust baseline models.

tagE: Enabling an Embodied Agent to Understand Human Instructions​

As the media landscape evolves, the study of news framing, key to shaping public opinion, has expanded. Our study presents a methodology for framing analysis in Spanish media using NLP to uncover the cultural and political factors shaping narratives. This method enhances linguistic inclusivity in framing studies and highlights Spanish media's role in public opinion, showing NLP's utility in diverse linguistic contexts and contributing to a more comprehensive global media analysis.

Cross-Linguistic Framing Analysis: Unveiling Political and Cultural Narratives in Spanish-Language News

Recently, deep learning-based language models have significantly enhanced text-to-SQL tasks, with promising applications in retrieving patient records within the medical domain. One notable challenge in such applications is discerning unanswerable queries. Through fine-tuning model, we demonstrate the feasibility of converting medical record inquiries into SQL queries. Additionally, we introduce an entropy-based method to identify and filter out unanswerable results. We further enhance result quality by filtering low-confidence SQL through log probability-based distribution, while grammatical and schema errors are mitigated by executing queries on the actual database.
We experimentally verified that our method can filter unanswerable questions, which can be widely utilized even when the parameters of the model are not accessible, and that it can be effectively utilized in practice.

ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling

The EHRSQL task aims to develop a dependable text-to-SQL model for Electronic Health Records (EHR) databases, which are crucial sources of clinical data that store patients' medical histories in hospitals. Large language models (LLM) have been proven to exhibit state-of-the-art performance for text-to-SQL tasks across various domains. To this end, we have developed a framework, SQL Generation through Classification Answer Selector by LLM (SCAS), which comprises two modules. The CAS module determines the answerability of the question, while the SG model generates the SQL query exclusively for answerable questions. Our system ranked 7th on the leaderboard with a Reliability Score of 53.21 on the official test set.

Saama Technologies at EHRSQL 2024: SQL Generation through Classification Answer Selector by LLM

Electronic Health Records (EHRs) are relational databases that store the entire medical histories of patients within hospitals. They record numerous aspects of patients' medical care, from hospital admission and diagnosis to treatment and discharge. While EHRs are vital sources of clinical data, exploring them beyond a predefined set of queries requires skills in query languages like SQL. To make information retrieval more accessible, one strategy is to build a question-answering system, possibly leveraging text-to-SQL models that can automatically translate natural language questions into corresponding SQL queries and use these queries to retrieve the answers. The EHRSQL 2024 shared task aims to advance and promote research in developing a question-answering system for EHRs using text-to-SQL modeling, capable of reliably providing requested answers to various healthcare professionals to improve their clinical work processes and satisfy their needs. Among more than 100 participants who applied to the shared task, eight teams completed the entire shared task processes and demonstrated a wide range of methods to effectively solve this task. In this paper, we describe the task of reliable text-to-SQL modeling, the dataset, and the methods and results of the participants. We hope this shared task will spur further research and insights into developing reliable question-answering systems for EHRs.

Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records

This paper outlines our submission to the MEDIQA2024 Multilingual and Multimodal Medical Answer Generation (M3G) shared task. We report results for two standalone solutions under the English category of the task, the first involving two consecutive API calls to the Claude 3 Opus API and the second involving training an image-disease label joint embedding in the style of CLIP for image classification. These two solutions scored 1st and 2nd place respectively on the competition leaderboard, substantially outperforming the next best solution. Additionally, we discuss insights gained from post-competition experiments. While the performance of these two described solutions have significant room for improvement due to the difficulty of the shared task and the challenging nature of medical visual question answering in general, we identify the multi-stage LLM approach and the CLIP image classification approach as promising avenues for further investigation.

WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models

This paper presents our approach for the 2024 ChemoTimelines shared task. Specifically, we explored using Large Language Models (LLMs) for temporal relation extraction. We evaluate multiple model variations based on how the training data is used. For instance, we transform the task into a question-answering problem and use QA pairs to extract chemo-related events and their temporal relations. Next, we add all the documents to each question-answer pair as examples in our training dataset. Finally, we explore adding unlabeled data for continued pretraining. Each addition is done iteratively. Our results show that adding the document helps, but unlabeled data does not yield performance improvements, possibly because we used only 1\% of the available data. Moreover, we find that instruction-tuned models still substantially underperform more traditional systems (e.g., EntityBERT).

UTSA-NLP at ChemoTimelines 2024: Evaluating Instruction-Tuned Language Models for Temporal Relation Extraction

In natural language processing applied to the clinical domain, utilizing large language models has emerged as a promising avenue for error detection and correction on clinical notes, a knowledge-intensive task for which annotated data is scarce. This paper presents MedReAct'N'MedReFlex, which leverages a suite of four LLM-based medical agents. The MedReAct agent initiates the process by observing, analyzing, and taking action, generating trajectories to guide the search to target a potential error in the clinical notes. Subsequently, the MedEval agent employs five evaluators to assess the targeted error and the proposed correction. In cases where MedReAct's actions prove insufficient, the MedReFlex agent intervenes, engaging in reflective analysis and proposing alternative strategies. Finally, the MedFinalParser agent formats the final output, preserving the original style while ensuring the integrity of the error correction process. One core component of our method is our RAG pipeline based on our ClinicalCorp corpora. Among other well-known sources containing clinical guidelines and information, we preprocess and release the open-source MedWiki dataset for clinical RAG application. Our results demonstrate the central role of our RAG approach with ClinicalCorp leveraged through the MedReAct'N'MedReFlex framework. It achieved the ninth rank on the MEDIQA-CORR 2024 final leaderboard.

IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task on the Shoulders of Medical Agents

The 2024 Shared Task on Chemotherapy Treatment Timeline Extraction aims to advance the state of the art of clinical event timeline extraction from the Electronic Health Records (EHRs). Specifically, this edition focuses on chemotherapy event timelines from EHRs of patients with breast, ovarian and skin cancers. These patient-level timelines present a novel challenge which involves tasks such as the extraction of relevant events, time expressions and temporal relations from each document and then summarizing over the documents. De-identified EHRs for 57,530 patients with breast and ovarian cancer spanning 2004-2020, and approximately 15,946 patients with melanoma spanning 2010-2020 were made available to participants after executing a Data Use Agreement. A subset of patients is annotated for gold entities, time expressions, temporal relations and patient-level timelines. The rest is considered unlabeled data. In Subtask1, gold chemotherapy event mentions and time expressions are provided (along with the EHR notes). Participants are asked to build the patient-level timelines using gold annotations as input. Thus, the subtask seeks to explore the topics of temporal relations extraction and timeline creation if event and time expression input is perfect. In Subtask2, which is the realistic real-world setting, only EHR notes are provided. Thus, the subtask aims at developing an end-to-end system for chemotherapy treatment timeline extraction from patient's EHR notes. There were 18 submissions for Subtask 1 and 9 submissions for Subtask 2. The organizers provided a baseline system. The teams employed a variety of methods including Logistic Regression, TF-IDF, n-grams, transformer models, zero-shot prompting with Large Language Models (LLMs), and instruction tuning. The gap in performance between prompting LLMs and finetuning smaller-sized LMs indicates that for a challenging task such as patient-level chemotherapy timeline extraction, more sophisticated LLMs or prompting techniques are necessary in order to achieve optimal results as finetuing smaller-sized LMs outperforms by a wide margin.

Overview of the 2024 Shared Task on Chemotherapy Treatment Timeline Extraction

The automatic identification of medical errors in clinical notes is crucial for improving the quality of healthcare services.LLMs emerge as a powerful artificial intelligence tool for automating this task. However, LLMs present vulnerabilities, high costs, and sometimes a lack of transparency. This article addresses the detection of medical errors through the fine-tuning approach, conducting a comprehensive comparison between various models and exploring in depth the components of the machine learning pipeline. The results obtained with the fine-tuned ClinicalBert and Gated recurrent units (Gru) models show an accuracy of 0.56 and 0.55, respectively. This approach not only mitigates the problems associated with the use of LLMs but also demonstrates how exhaustive iteration in critical phases of the pipeline, especially in feature selection, can facilitate the automation of clinical record analysis.

Underline digital video library

The Vision Thing: Finding and Pursuing your Research Passion

Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users

tagE: Enabling an Embodied Agent to Understand Human Instructions

Cross-Linguistic Framing Analysis: Unveiling Political and Cultural Narratives in Spanish-Language News

ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling

Saama Technologies at EHRSQL 2024: SQL Generation through Classification Answer Selector by LLM

Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records

WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models

UTSA-NLP at ChemoTimelines 2024: Evaluating Instruction-Tuned Language Models for Temporal Relation Extraction

IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task on the Shoulders of Medical Agents

Overview of the 2024 Shared Task on Chemotherapy Treatment Timeline Extraction

VerbaNexAI at MEDIQA-CORR: Efficacy of GRU with BioWordVec and ClinicalBERT in Error Correction in Clinical Notes

Underline digital video library

The Vision Thing: Finding and Pursuing your Research Passion

Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users

tagE: Enabling an Embodied Agent to Understand Human Instructions​

Cross-Linguistic Framing Analysis: Unveiling Political and Cultural Narratives in Spanish-Language News

ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling

Saama Technologies at EHRSQL 2024: SQL Generation through Classification Answer Selector by LLM

Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records

WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models

UTSA-NLP at ChemoTimelines 2024: Evaluating Instruction-Tuned Language Models for Temporal Relation Extraction

IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task on the Shoulders of Medical Agents

Overview of the 2024 Shared Task on Chemotherapy Treatment Timeline Extraction

VerbaNexAI at MEDIQA-CORR: Efficacy of GRU with BioWordVec and ClinicalBERT in Error Correction in Clinical Notes

tagE: Enabling an Embodied Agent to Understand Human Instructions