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Found 22 papers, 6 papers with code
Benchmarking Language-agnostic Intent Classification for Virtual Assistant Platforms
Current virtual assistant (VA) platforms are beholden to the limited number of languages they support.
Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall
By further decoupling model known and unknown knowledge, we find the degradation is attributed to exemplars that contradict a model's known knowledge, as well as the number of such exemplars.
Enhanced Urban Region Profiling with Adversarial Contrastive Learning
Finally, we jointly optimize attentive supervised and adversarial contrastive learning to encourage the model to capture the high-level semantics of region embeddings while ignoring the noisy and irrelevant details.
UNITE: A Unified Benchmark for Text-to-SQL Evaluation
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures.
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries.
Curvilinear object segmentation in medical images based on ODoS filter and deep learning network
Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions.
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data.
Improved Text Classification via Contrastive Adversarial Training
We propose a simple and general method to regularize the fine-tuning of Transformer-based encoders for text classification tasks.
Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction
We propose a novel method for automatic separation of pulmonary arteries and veins from chest CT images.
Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing
To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches.
Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation
In addition, explanations of CDAM features proved that the shape and density of nodule regions were two critical factors that influence a nodule to be inferred as malignant, which conforms with the diagnosis cognition of experienced radiologists.
Multilingual Transfer Learning for QA Using Translation as Data Augmentation
Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages.
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations
Secondly, even with large training data, the intent detection models can see a different distribution of test data when being deployed in the real world, leading to poor accuracy.
Towards building a Robust Industry-scale Question Answering System
Building on top of the powerful BERTQA model, GAAMA provides a ∼2. 0{\%} absolute boost in F1 over the industry-scale state-of-the-art (SOTA) system on NQ.
Multilingual BERT Post-Pretraining Alignment
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models.
ARES: A Reading Comprehension Ensembling Service
We introduce ARES (A Reading Comprehension Ensembling Service): a novel Machine Reading Comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2. 3 points.
The TechQA Dataset
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
Ensembling Strategies for Answering Natural Questions
Many of the top question answering systems today utilize ensembling to improve their performance on tasks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) challenges.
Frustratingly Easy Natural Question Answering
Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa.
Ranked #5 on
Question Answering
on Natural Questions (long)
Span Selection Pre-training for Question Answering
BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA).
CFO: A Framework for Building Production NLP Systems
This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments.
Multi-Granular Text Encoding for Self-Explaining Categorization
Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence.
