Papers Machine Reading Comprehension
“Machine Reading Comprehension” 태그가 달린 논문 562편 · 필터 해제
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model
Despite the significant progress made by transformer models in machine reading comprehension tasks, they still fall short in handling complex reasoning tasks due to the absence of explicit knowledge in the input sequence…
Common Sense ReasoningGraph AttentionMachine Reading ComprehensionPosition+1ZeQR: Zero-shot Query Reformulation for Conversational Search
As the popularity of voice assistants continues to surge, conversational search has gained increased attention in Information Retrieval. However, data sparsity issues in conversational search significantly hinder the pro…
Conversational SearchInformation RetrievalMachine Reading ComprehensionReading Comprehension+1Teach model to answer questions after comprehending the document
Multi-choice Machine Reading Comprehension (MRC) is a challenging extension of Natural Language Processing (NLP) that requires the ability to comprehend the semantics and logical relationships between entities in a given…
Knowledge DistillationMachine Reading ComprehensionReading ComprehensionIDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
In the field of machine reading comprehension (MRC), existing systems have surpassed the average performance of human beings in many tasks like SQuAD. However, there is still a long way to go when it comes to logical rea…
Logical ReasoningMachine Reading ComprehensionReading ComprehensionSentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension
The traditional way of sentence-level event detection involves two important subtasks: trigger identification and trigger classifications, where the identified event trigger words are used to classify event types from se…
Event DetectionMachine Reading ComprehensionPrompt LearningReading Comprehension+1SciMRC: Multi-perspective Scientific Machine Reading Comprehension
Scientific machine reading comprehension (SMRC) aims to understand scientific texts through interactions with humans by given questions. As far as we know, there is only one dataset focused on exploring full-text scienti…
Machine Reading ComprehensionReading ComprehensionModeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension
Machine reading comprehension (MRC) poses new challenges over logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them. Due to the complexi…
Logical ReasoningMachine Reading ComprehensionNatural Language InferenceReading Comprehension+1Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first find mentions in the given input document and then link the mentions t…
Entity LinkingEntity RetrievalKnowledge GraphsMachine Reading Comprehension+2Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models
Conventional knowledge distillation (KD) methods require access to the internal information of teachers, e.g., logits. However, such information may not always be accessible for large pre-trained language models (PLMs). …
Data AugmentationKnowledge DistillationMachine Reading ComprehensionNatural Language Understanding+1Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite
Label error is a ubiquitous problem in annotated data. Large amounts of label error substantially degrades the quality of deep learning models. Existing methods to tackle the label error problem largely focus on the clas…
Label Error DetectionMachine Reading ComprehensionQuestion AnsweringReading Comprehension+1Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However, current methods have trouble answering complex questions. Therefore, we introduce the Knowing-how & Knowing-that ta…
BenchmarkingMachine Reading ComprehensionReading ComprehensionHow Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
The multi-answer phenomenon, where a question may have multiple answers scattered in the document, can be well handled by humans but is challenging enough for machine reading comprehension (MRC) systems. Despite recent p…
Machine Reading ComprehensionReading ComprehensionA Causal View of Entity Bias in (Large) Language Models
Entity bias widely affects pretrained (large) language models, causing them to rely on (biased) parametric knowledge to make unfaithful predictions. Although causality-inspired methods have shown great potential to mitig…
Machine Reading ComprehensionMemorizationReading ComprehensionRelation ExtractionMachine Reading Comprehension using Case-based Reasoning
We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our method (CBR-MRC) builds upon the hypothesis …
AttributeMachine Reading ComprehensionReading ComprehensionmPMR: A Multilingual Pre-trained Machine Reader at Scale
We present multilingual Pre-trained Machine Reader (mPMR), a novel method for multilingual machine reading comprehension (MRC)-style pre-training. mPMR aims to guide multilingual pre-trained language models (mPLMs) to pe…
ClassificationMachine Reading ComprehensionNatural Language UnderstandingReading Comprehension+2EMBRACE: Evaluation and Modifications for Boosting RACE
When training and evaluating machine reading comprehension models, it is very important to work with high-quality datasets that are also representative of real-world reading comprehension tasks. This requirement includes…
Machine Reading ComprehensionMultiple-choiceReading ComprehensionSkillQG: Learning to Generate Question for Reading Comprehension Assessment
We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. Existing question generation systems widel…
Machine Reading ComprehensionQuestion AnsweringQuestion GenerationQuestion-Generation+1Adaptive loose optimization for robust question answering
Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current …
Extractive Question-AnsweringMachine Reading ComprehensionQuestion AnsweringReading Comprehension+2NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension
Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research focuses on utilizing massive extra data,…
Machine Reading Comprehensionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical reasoning task. Existing methods based on…
DecoderGraph Representation LearningMachine Reading ComprehensionQuestion Answering+2