Papers Machine Reading Comprehension
“Machine Reading Comprehension” 태그가 달린 논문 562편 · 필터 해제
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing met…
Decision MakingMachine Reading ComprehensionReading ComprehensionA Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction
Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause analysis, which extracts potential emotion-cause pairs from an emotional document. Most recent studies use end-to-end methods to tackle the ECPE t…
Emotion-Cause Pair ExtractionMachine Reading ComprehensionReading ComprehensionA Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension
The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years. Unintended correlations in the data enable models to easily solve tasks that were meant to exhibit advanced …
Machine Reading ComprehensionReading ComprehensionUnsupervised Domain Adaptation on Question-Answering System with Conversation Data
Machine reading comprehension (MRC) is a task for question answering that finds answers to questions from documents of knowledge. Most studies on the domain adaptation of MRC require documents describing knowledge of the…
Domain AdaptationMachine Reading ComprehensionQuestion AnsweringReading Comprehension+1Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension
The concepts in knowledge graphs (KGs) enable machines to understand natural language, and thus play an indispensable role in many applications. However, existing KGs have the poor coverage of concepts, especially fine-g…
DescriptiveKnowledge GraphsMachine Reading ComprehensionReading ComprehensionTrigger-free Event Detection via Derangement Reading Comprehension
Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding actual happenings in real life. However, mainstream event detection models require high-quality expert human annotat…
Event DetectionMachine Reading ComprehensionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension
Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common practice to employ a predictor to draw i…
Machine Reading ComprehensionReading ComprehensionContinual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation
Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the development of real-world MRC systems. However…
Domain AdaptationIncremental LearningMachine Reading ComprehensionReading ComprehensionAct-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking
As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue. Representations of dialogu…
Dialogue State TrackingMachine Reading ComprehensionMulti-domain Dialogue State TrackingReading Comprehension+1To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning
Machine Reading Comprehension with Unanswerable Questions is a difficult NLP task, challenged by the questions which can not be answered from passages. It is observed that subtle literal changes often make an answerable …
Contrastive LearningMachine Reading ComprehensionReading ComprehensionMRCLens: an MRC Dataset Bias Detection Toolkit
Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-…
Bias DetectionMachine Reading ComprehensionReading ComprehensionExploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained Models
Most of the Chinese pre-trained models adopt characters as basic units for downstream tasks. However, these models ignore the information carried by words and thus lead to the loss of some important semantics. In this pa…
Machine Reading ComprehensionNatural Language InferenceReading ComprehensionSentence+2An Understanding-Oriented Robust Machine Reading Comprehension Model
Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this paper, we propose an understanding-oriented machine reading comprehension model to addr…
Machine Reading ComprehensionmodelMulti-Task LearningNatural Language Inference+2JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions
This paper describes our system in the SemEval-2022 Task 12: ‘linking mathematical symbols to their descriptions’, achieving first on the leaderboard for all the subtasks comprising named entity extraction (NER) and rela…
Joint Entity and Relation ExtractionMachine Reading ComprehensionNERReading Comprehension+1A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction
Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context. Th…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Aspect Sentiment Triplet ExtractionMachine Reading Comprehension+3OPERA: 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 ParsingEnd-to-End Chinese Speaker Identification
Speaker identification (SI) in texts aims to identify the speaker(s) for each utterance in texts. Previous studies divide SI into several sub-tasks (e.g., quote extraction, named entity recognition, gender identification…
coreference-resolutionCoreference ResolutionMachine Reading Comprehensionnamed-entity-recognition+4Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop
We present our experience as annotators in the creation of high-quality, adversarial machine-reading-comprehension data for extractive QA for Task 1 of the First Workshop on Dynamic Adversarial Data Collection (DADC). DA…
Machine Reading ComprehensionReading ComprehensionContextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering
Biomedical Question Answering aims to obtain an answer to the given question from the biomedical domain. Due to its high requirement of biomedical domain knowledge, it is difficult for the model to learn domain knowledge…
Cloze TestMachine Reading ComprehensionQuestion AnsweringUnsupervised Pre-trainingAdversarial Self-Attention for Language Understanding
Deep neural models (e.g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness. This paper advances the self-attention…
Machine Reading ComprehensionNamed Entity Recognition (NER)Natural Language InferenceParaphrase Identification+3