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Found 21 papers, 7 papers with code
Automatic True/False Question Generation for Educational Purpose
In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials.
CXR Data Annotation and Classification with Pre-trained Language Models
Clinical data annotation has been one of the major obstacles for applying machine learning approaches in clinical NLP.
Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization
In this work, we introduce a joint paraphrasing task of creole translation and text normalization of Singlish messages, which can shed light on how to process other language varieties and dialects.
Winnowing Knowledge for Multi-choice Question Answering
However, the current reasoning models suffer from the noises in the retrieved knowledge.
Evidence-based Interpretable Open-domain Fact-checking with Large Language Models
Universal fact-checking systems for real-world claims face significant challenges in gathering valid and sufficient real-time evidence and making reasoned decisions.
Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models
This study investigates machine translation between related languages i. e., languages within the same family that share linguistic characteristics such as word order and lexical similarity.
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation
In addition, we propose Conv-Distinct, a novel evaluation metric for CQG, to evaluate the diversity of the generated conversation from a context.
CoHS-CQG: Context and History Selection for Conversational Question Generation
While previous studies mainly focus on how to model the flow and alignment of the conversation, there has been no thorough study to date on which parts of the context and history are necessary for the model.
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model
Numerous recent work on unsupervised machine translation (UMT) implies that competent unsupervised translations of low-resource and unrelated languages, such as Nepali or Sinhala, are only possible if the model is trained in a massive multilingual environment, where these low-resource languages are mixed with high-resource counterparts.
Improving Lexical Embeddings for Robust Question Answering
Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance.
Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States
Neural models for text generation are often designed in an end-to-end fashion, typically with zero control over intermediate computations, limiting their practical usability in downstream applications.
Addressing the Vulnerability of NMT in Input Perturbations
Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations.
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks
While neural approaches have achieved significant improvement in machine comprehension tasks, models often work as a black-box, resulting in lower interpretability, which requires special attention in domains such as healthcare or education.
Cross-model Back-translated Distillation for Unsupervised Machine Translation
Recent unsupervised machine translation (UMT) systems usually employ three main principles: initialization, language modeling and iterative back-translation, though they may apply them differently.
Data Diversification: A Simple Strategy For Neural Machine Translation
Our method achieves state-of-the-art BLEU scores of 30. 7 and 43. 7 in the WMT'14 English-German and English-French translation tasks, respectively.
Ranked #9 on
Machine Translation
on WMT2014 English-German
Sentiment Aware Neural Machine Translation
We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods.
Revisit Automatic Error Detection for Wrong and Missing Translation -- A Supervised Approach
While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues.
Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations
Such linguistic characteristics of dialogue topics make sentence-level extractive summarization approaches used in spoken documents ill-suited for summarizing conversations.
Named-Entity Tagging and Domain adaptation for Better Customized Translation
Customized translation need pay spe-cial attention to the target domain ter-minology especially the named-entities for the domain.
