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Found 21 papers, 6 papers with code
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
PANDA (Pedantic ANswer-correctness Determination and Adjudication):Improving Automatic Evaluation for Question Answering and Text Generation
Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current answer correctness (AC) metrics do not align with human judgments, particularly verbose, free form answers from large language models (LLM).
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues
To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances.
BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification
Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis.
ALEX: Active Learning based Enhancement of a Model's Explainability
An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a bootstrapping manner.
Towards Incorporating Entity-specific Knowledge Graph Information in Predicting Drug-Drug Interactions
In this paper, we explore how to incorporate domain knowledge of the biomedical entities (such as drug, disease, genes), obtained from Knowledge Graph (KG) Embeddings, for predicting Drug-Drug Interaction from textual corpus.
Medical Entity Linking using Triplet Network
Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB).
BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information
Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medical domain.
End-to-End NLP Knowledge Graph Construction
This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers.
Multi-Objective Few-shot Learning for Fair Classification
In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e. g., race, gender etc.).
A Linguistic Annotation Framework to Study Interactions in Multilingual Healthcare Conversational Forums
In recent years, remote digital healthcare using online chats has gained momentum, especially in the Global South.
Extracting Semantic Aspects for Structured Representation of Clinical Trial Eligibility Criteria
Eligibility criteria in the clinical trials specify the characteristics that a patient must or must not possess in order to be treated according to a standard clinical care guideline.
Global Readiness of Language Technology for Healthcare: What would it Take to Combat the Next Pandemic?
The COVID-19 pandemic has brought out both the best and worst of language technology (LT).
Language Patterns and Behaviour of the Peer Supporters in Multilingual Healthcare Conversational Forums
In this work, we conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp groups in Kenya comprising of youth living with HIV.
Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages
Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare.
InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction
Learning template based information extraction from documents is a crucial yet difficult task.
How the Advent of Ubiquitous Large Language Models both Stymie and Turbocharge Dynamic Adversarial Question Generation
Dynamic adversarial question generation, where humans write examples to stump a model, aims to create examples that are realistic and informative.
CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering
Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current evaluation metrics to determine answer equivalence (AE) often do not align with human judgments, particularly more verbose, free-form answers from large language models (LLM).
MunTTS: A Text-to-Speech System for Mundari
We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family.
How much reliable is ChatGPT's prediction on Information Extraction under Input Perturbations?
In this paper, we assess the robustness (reliability) of ChatGPT under input perturbations for one of the most fundamental tasks of Information Extraction (IE) i. e. Named Entity Recognition (NER).
