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Found 17 papers, 2 papers with code
Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions
Human assessment remains the most trusted form of evaluation in NLG, but highly diverse approaches and a proliferation of different quality criteria used by researchers make it difficult to compare results and draw conclusions across papers, with adverse implications for meta-evaluation and reproducibility.
An Ontology-driven Treatment Article Retrieval System for Precision Oncology
This paper presents an ontology-driven treatment article retrieval system developed and experimented using the data and ground truths provided by the TREC 2017 precision medicine track.
SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks
The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of natural language processing (NLP) algorithms.
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.
Ranked #16 on
Natural Language Inference
on SNLI
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario.
Condensed Memory Networks for Clinical Diagnostic Inferencing
We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory.
Neural Clinical Paraphrase Generation with Attention
Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact.
Neural Paraphrase Generation with Stacked Residual LSTM Networks
To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation.
Complex Question Answering: Unsupervised Learning Approaches and Experiments
Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a set of documents with a minimum loss of relevant information.
