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Found 13 papers, 7 papers with code
Reinforced Mnemonic Reader for Machine Reading Comprehension
In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.
Ranked #17 on
Question Answering
on SQuAD1.1 dev
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification
Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence.
Aspect-Based Sentiment Analysis (ABSA)
Aspect Term Extraction and Sentiment Classification
+2
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
This paper considers the reading comprehension task in which multiple documents are given as input.
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
Rapid progress has been made in the field of reading comprehension and question answering, where several systems have achieved human parity in some simplified settings.
Ranked #8 on
Question Answering
on DROP Test
Snapshot Ptychography on Array cameras
We use convolutional neural networks to recover images optically down-sampled by $6. 7\times$ using coherent aperture synthesis over a 16 camera array.
Array Camera Image Fusion using Physics-Aware Transformers
We demonstrate a physics-aware transformer for feature-based data fusion from cameras with diverse resolution, color spaces, focal planes, focal lengths, and exposure.
KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate.
Read + Verify: Machine Reading Comprehension with Unanswerable Questions
Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred.
Ranked #11 on
Question Answering
on SQuAD2.0 dev
Attention-Guided Answer Distillation for Machine Reading Comprehension
Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models.
Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction
The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e. g., token-to-token and labelto-token).
Interactive Contrastive Learning for Self-supervised Entity Alignment
Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments.
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training
Secondly, we propose a hybrid selection strategy in the extractor, which not only makes full use of span boundary but also improves the ability of long entity recognition.
Adaptive Threshold Selective Self-Attention for Chinese NER
Recently, Transformer has achieved great success in Chinese named entity recognition (NER) owing to its good parallelism and ability to model long-range dependencies, which utilizes self-attention to encode context.
Chinese Named Entity Recognition
named-entity-recognition
+2
