Papers Machine Reading Comprehension
“Machine Reading Comprehension” 태그가 달린 논문 562편 · 필터 해제
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics
Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part o…
Machine Reading ComprehensionReading ComprehensionA Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches
Machine reading comprehension (MRC) is a long-standing topic in natural language processing (NLP). The MRC task aims to answer a question based on the given context. Recently studies focus on multi-hop MRC which is a mor…
Machine Reading ComprehensionReading ComprehensionSurveyGENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual spans, phrases, or words, concatenated by…
Conditional Text GenerationData AugmentationLanguage ModelingLanguage Modelling+10Feature-augmented Machine Reading Comprehension with Auxiliary Tasks
While most successful approaches for machine reading comprehension rely on single training objective, it is assumed that the encoder layer can learn great representation through the loss function we define in the predict…
Machine Reading ComprehensionReading ComprehensionIDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become one of the essential tasks in Natural Language Understanding (NLU) as it is often included in several NLU benchmarks (Liang et al., 2020; Wilie et al., 2020). However, most …
Machine Reading ComprehensionNatural Language UnderstandingQuestion GenerationReading ComprehensionNEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
This paper describes NEREL-BIO -- an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL by introducing domain-specif…
Machine Reading ComprehensionReading ComprehensionMultitask Pre-training of Modular Prompt for Chinese Few-Shot Learning
Prompt tuning is a parameter-efficient approach to adapting pre-trained language models to downstream tasks. Although prompt tuning has been shown to match the performance of full model tuning when training data is suffi…
Few-Shot LearningMachine Reading ComprehensionReading ComprehensionRethinking Annotation: Can Language Learners Contribute?
Researchers have traditionally recruited native speakers to provide annotations for widely used benchmark datasets. However, there are languages for which recruiting native speakers can be difficult, and it would help to…
Machine Reading Comprehensionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking
Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data. Existing few-shot methods mainly transfer knowledge learned from external labeled dialogue data (e.g., fr…
Dialogue State TrackingMachine Reading ComprehensionQuestion AnsweringReading Comprehension+1U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension
More tasks in Machine Reading Comprehension(MRC) require, in addition to answer prediction, the extraction of evidence sentences that support the answer. However, the annotation of supporting evidence sentences is usuall…
Machine Reading ComprehensionReading ComprehensionSentenceModular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts
In this work we present a Mixture of Task-Aware Experts Network for Machine Reading Comprehension on a relatively small dataset. We particularly focus on the issue of common-sense learning, enforcing the common ground kn…
Common Sense ReasoningMachine Reading ComprehensionReading ComprehensionTransfer Learning+1基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)
“目前常见的机器阅读理解数据增强方法如回译,单独对文章或者问题进行数据增强,没有考虑文章、问题和选项三元组之间的联系。因此,本文探索了一种利用三元组联系进行文章句子筛选的数据增强方法,通过比较文章与问题以及选项的相似度,选取文章中与二者联系紧密的句子。同时为了使不同选项的三元组区别增大,我们选用了正则化Dropout的策略。实验结果表明,在RACE数据集上的准确率可提高3.8%。”
Data AugmentationMachine Reading ComprehensionReading ComprehensionSentence基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)
“机器阅读理解(Machine Reading Comprehension, MRC)任务旨在让机器回答给定上下文的问题来测试机器理解自然语言的能力。目前,基于大规模预训练语言模型的神经机器阅读理解模型已经取得重要进展,但在涉及答案要素、线索要素和问题要素跨标点句、远距离关联时,答案抽取的准确率还有待提升。本文通过篇章内话头话体结构分析,建立标点句间远距离关联关系、补全共享缺失成分,辅助机器阅读理解答案抽取;设计和实现融合话头话体结构信…
Machine Reading ComprehensionReading ComprehensionDIFM:An effective deep interaction and fusion model for sentence matching
“Natural language sentence matching is the task of comparing two sentences and identifying the relationship between them. It has a wide range of applications in natural language processing tasks such as reading comprehen…
Machine Reading ComprehensionNatural Language InferenceReading ComprehensionSentenceTo What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.
Reasoning and knowledge-related skills are considered as two fundamental skills for natural language understanding (NLU) tasks such as machine reading comprehension (MRC) and natural language inference (NLI). However, it…
DiagnosticLogical ReasoningMachine Reading ComprehensionNatural Language Inference+2DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition
Cross-domain named entity recognition aims to improve performance in a target domain with shared knowledge from a well-studied source domain. The previous sequence-labeling based method focuses on promoting model paramet…
Cross-Domain Named Entity RecognitionMachine Reading Comprehensionnamed-entity-recognitionNamed Entity Recognition+2Aspect-based Sentiment Analysis as Machine Reading Comprehension
Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-Pr…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Machine Reading ComprehensionPrompt Learning+2Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning
Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Proce…
Data AugmentationMachine Reading ComprehensionMultiple-choiceReading Comprehension+4View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond
Existing works on dialogue summarization often follow the common practice in document summarization and view the dialogue, which comprises utterances of different speakers, as a single utterance stream ordered by time. H…
Document SummarizationMachine Reading ComprehensionReading ComprehensionRobust Domain Adaptation for Machine Reading Comprehension
Most domain adaptation methods for machine reading comprehension (MRC) use a pre-trained question-answer (QA) construction model to generate pseudo QA pairs for MRC transfer. Such a process will inevitably introduce mism…
Domain AdaptationMachine Reading ComprehensionReading Comprehension