Papers Machine Reading Comprehension
“Machine Reading Comprehension” 태그가 달린 논문 562편 · 필터 해제
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions
Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types. We present a multilingual machine reading c…
GPUMachine Reading ComprehensionReading ComprehensionHRCA+: Advanced Multiple-choice Machine Reading Comprehension Method
Multiple-choice question answering (MCQA) for machine reading comprehension (MRC) is challenging. It requires a model to select a correct answer from several candidate options related to text passages or dialogue. To sel…
Machine Reading ComprehensionMultiple-choiceMultiple Choice Question Answering (MCQA)Question Answering+2Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT
In this paper, we focused on news reported when stock prices fluctuate significantly. The news reported when stock prices change is a very useful source of information on what factors cause stock prices to change. Howeve…
Machine Reading ComprehensionReading ComprehensionQur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an
Motivated by the resurgence of the machine reading comprehension (MRC) research, we have organized the first Qur’an Question Answering shared task, “Qur’an QA 2022”. The task in its first year aims to promote state-of-th…
Machine Reading ComprehensionQuestion AnsweringReading ComprehensionDTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain
The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the …
Ensemble LearningMachine Reading ComprehensionNatural Language UnderstandingQuestion Answering+2Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model
In recent years, new deep learning methods and pre-training language models have been emerging in the field of natural language processing (NLP). These methods and models can greatly improve the accuracy of automatic wor…
Language ModelingLanguage ModellingMachine Reading ComprehensionPart-Of-Speech Tagging+4FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm
Financial named entity recognition (FinNER) from literature is a challenging task in the field of financial text information extraction, which aims to extract a large amount of financial knowledge from unstructured texts…
Machine Reading Comprehensionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+1NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension
NER has been traditionally formulated as a sequence labeling task. However, there has been recent trend in posing NER as a machine reading comprehension task (Wang et al., 2020; Mengge et al., 2020), where entity name (o…
AttributeMachine Reading Comprehensionnamed-entity-recognitionNamed Entity Recognition+3DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain
The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the …
Ensemble LearningMachine Reading ComprehensionNatural Language UnderstandingQuestion Answering+2KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-trained Language Models (PLMs). However, m…
Contrastive LearningExtractive Question-AnsweringFew-Shot LearningLanguage Modeling+5Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reason…
Logical ReasoningMachine Reading ComprehensionReading ComprehensionVocal Bursts Valence PredictionClozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task. Unfortunately, existing adaptations mainly involve deterministic rules …
Data AugmentationMachine Reading ComprehensionMultiple-choiceReading ComprehensionGraph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model
Coreference resolution such as for anaphora has been an essential challenge that is commonly found in conversational machine reading comprehension (CMRC). This task aims to determine the referential entity to which a pro…
coreference-resolutionCoreference ResolutionGraph Neural NetworkLanguage Modeling+3G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents
Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task. Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehensi…
Machine Reading ComprehensionReading ComprehensionResponse GenerationAnswer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension
Machine reading comprehension (MRC) has drawn a lot of attention as an approach for assessing the ability of systems to understand natural language. Usually systems focus on selecting the correct answer to a question giv…
Machine Reading ComprehensionMultiple-choiceReading ComprehensionHave my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension
Argument pair extraction (APE) aims to automatically mine argument pairs from two interrelated argumentative documents. Existing studies typically identify argument pairs indirectly by predicting sentence-level relations…
Argument MiningArgument Pair Extraction (APE)Machine Reading ComprehensionReading Comprehension+1OPERA:Operation-Pivoted Discrete Reasoning over Text
Machine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task. According to this nature, semantic parsing-based methods p…
Machine Reading ComprehensionReading ComprehensionSemantic ParsingXLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source
Question answering (QA) is a natural language understanding task within the fields of information retrieval and information extraction that has attracted much attention from the computational linguistics and artificial i…
Information RetrievalMachine Reading ComprehensionNatural Language UnderstandingOpen-Domain Question Answering+3The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer
Large multilingual language models such as mBERT or XLM-R enable zero-shot cross-lingual transfer in various IR and NLP tasks. Cao et al. (2020) proposed a data- and compute-efficient method for cross-lingual adjustment …
Continual LearningCross-Lingual TransferMachine Reading ComprehensionNatural Language Inference+3Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling
Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge …
Contrastive LearningLanguage ModelingLanguage ModellingMachine Reading Comprehension+1