Papers Machine Reading Comprehension
“Machine Reading Comprehension” 태그가 달린 논문 562편 · 필터 해제
QASE Enhanced PLMs: Improved Control in Text Generation for MRC
To address the challenges of out-of-control generation in generative models for machine reading comprehension (MRC), we introduce the Question-Attended Span Extraction (QASE) module. Integrated during the fine-tuning of …
Machine Reading ComprehensionReading ComprehensionText GenerationVlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension
This paper presents the development process of a Vietnamese spoken language corpus for machine reading comprehension (MRC) tasks and provides insights into the challenges and opportunities associated with using real-worl…
ArticlesMachine Reading ComprehensionReading ComprehensionMajority or Minority: Data Imbalance Learning Method for Named Entity Recognition
Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail…
Language ModellingMachine Reading Comprehensionnamed-entity-recognitionNamed Entity Recognition+3Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings
In Natural Language Processing (NLP), Machine Reading Comprehension (MRC) is the task of answering a question based on a given context. To handle questions in the medical domain, modern language models such as BioBERT, S…
Knowledge Graph EmbeddingsKnowledge GraphsMachine Reading ComprehensionMedical Question Answering+3Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. Methods We formulated 7 key…
AllLanguage ModellingLarge Language ModelMachine Reading Comprehension+3Towards Robust Text Retrieval with Progressive Learning
Retrieval augmentation has become an effective solution to empower large language models (LLMs) with external and verified knowledge sources from the database, which overcomes the limitations and hallucinations of LLMs i…
Machine Reading ComprehensionQuestion AnsweringReading ComprehensionRetrieval+1Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension
In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data from the source language to the target, or…
Cross-Lingual TransferMachine Reading ComprehensionMachine TranslationReading Comprehension+1Mirror: A Universal Framework for Various Information Extraction Tasks
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations. Meanwhile, this divergence leads to information waste and increases difficulties in …
Machine Reading ComprehensionReading ComprehensionTripletMPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
The large language models have achieved superior performance on various natural language tasks. One major drawback of such approaches is they are resource-intensive in fine-tuning new datasets. Soft-prompt tuning present…
Machine Reading ComprehensionReading ComprehensionMulti-grained Evidence Inference for Multi-choice Reading Comprehension
Multi-choice Machine Reading Comprehension (MRC) is a major and challenging task for machines to answer questions according to provided options. Answers in multi-choice MRC cannot be directly extracted in the given passa…
Machine Reading ComprehensionMulti-Choice MRCReading ComprehensionSentenceGuiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut Triggers
Recent applications of LLMs in Machine Reading Comprehension (MRC) systems have shown impressive results, but the use of shortcuts, mechanisms triggered by features spuriously correlated to the true label, has emerged as…
Machine Reading ComprehensionReading ComprehensionExplaining Interactions Between Text Spans
Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (…
Community DetectionDecision MakingExplainable Artificial Intelligence (XAI)Explanation Generation+5Instructive Dialogue Summarization with Query Aggregations
Conventional dialogue summarization methods directly generate summaries and do not consider user's specific interests. This poses challenges in cases where the users are more focused on particular topics or aspects. With…
Machine Reading ComprehensionReading ComprehensionText SummarizationHierarchical Evaluation Framework: Best Practices for Human Evaluation
Human evaluation plays a crucial role in Natural Language Processing (NLP) as it assesses the quality and relevance of developed systems, thereby facilitating their enhancement. However, the absence of widely accepted hu…
Machine Reading ComprehensionReading ComprehensionNamed Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types (e.g., organization or person name). Recently, many works have been proposed to shape the NER as a machine re…
Machine Reading ComprehensionMulti-Task Learningnamed-entity-recognitionNamed Entity Recognition+3Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change
Pir\'a is a reading comprehension dataset focused on the ocean, the Brazilian coast, and climate change, built from a collection of scientific abstracts and reports on these topics. This dataset represents a versatile la…
Generative Question AnsweringInformation RetrievalMachine Reading ComprehensionMultiple-choice+2Multi-turn Dialogue Comprehension from a Topic-aware Perspective
Dialogue related Machine Reading Comprehension requires language models to effectively decouple and model multi-turn dialogue passages. As a dialogue development goes after the intentions of participants, its topic may n…
Machine Reading ComprehensionReading ComprehensionThe Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this da…
BelebeleCross-Lingual TransferMachine Reading ComprehensionMultiple-choice+2Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot scenarios. The objective of this study …
Few-Shot LearningMachine Reading Comprehensionnamed-entity-recognitionNamed Entity Recognition+1Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias
Machine Reading Comprehension (MRC) models tend to take advantage of spurious correlations (also known as dataset bias or annotation artifacts in the research community). Consequently, these models may perform the MRC ta…
Machine Reading ComprehensionPositionReading ComprehensionSentence