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TitleStatusHype
Learning Sample Importance for Cross-Scenario Video Temporal Grounding—0
Development of an Extractive Clinical Question Answering Dataset with Multi-Answer and Multi-Focus Questions—0
A New Amharic Speech Emotion Dataset and Classification Benchmark—0
Sign Language Video Retrieval with Free-Form Textual Queries—0
Imagined versus Remembered Stories: Quantifying Differences in Narrative Flow—0
SMDT: Selective Memory-Augmented Neural Document Translation—0
Auto-ABSA: Cross-Domain Aspect Detection and Sentiment Analysis Using Auxiliary SentencesCode0
Weisfeiler-leman in the bamboo: Novel AMR graph metrics and a benchmark for AMR graph similarity.Code0
Exploring Motion and Appearance Information for Temporal Sentence Grounding—0
Toxicity Detection for Indic Multilingual Social Media Content—0
Memory-Guided Semantic Learning Network for Temporal Sentence Grounding—0
Weakly-Supervised Generation and Grounding of Visual Descriptions With Conditional Generative Models—0
DIFNet: Boosting Visual Information Flow for Image Captioning—0
Semi-Supervised Video Paragraph Grounding With Contrastive Encoder—0
Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias IdentificationCode0
QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task—0
Contextual Sentence Analysis for the Sentiment Prediction on Financial Data—0
Combining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing—0
Automatic extraction of requirements expressed in industrial standards : a way towards machine readable standards ?—0
More Than Words: Towards Better Quality Interpretations of Text Classifiers—0
Toward Educator-focused Automated Scoring Systems for Reading and Writing—0
On Cross-Lingual Retrieval with Multilingual Text EncodersCode0
Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents—0
Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian—0
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval—0
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