Search Results for author: Lin Pan

Found 15 papers, 3 papers with code

Improved Text Classification via Contrastive Adversarial Training

no code implementations21 Jul 2021 Lin Pan, Chung-Wei Hang, Avirup Sil, Saloni Potdar, Mo Yu

We propose a simple and general method to regularize the fine-tuning of Transformer-based encoders for text classification tasks.

Contrastive Learning Intent Classification +2

Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing

no code implementations25 Feb 2021 Jinquan Guo, Rongda Fu, Lin Pan, Shaohua Zheng, Liqin Huang, Bin Zheng, Bingwei He

To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches.

Computed Tomography (CT)

Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation

no code implementations22 Feb 2021 Shaohua Zheng, Zhiqiang Shen, Chenhao Peia, Wangbin Ding, Haojin Lin, Jiepeng Zheng, Lin Pan, Bin Zheng, Liqin Huang

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.

Lung Cancer Diagnosis

Multilingual Transfer Learning for QA Using Translation as Data Augmentation

no code implementations10 Dec 2020 Mihaela Bornea, Lin Pan, Sara Rosenthal, Radu Florian, Avirup Sil

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.

Cross-Lingual Transfer Data Augmentation +4

Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations

1 code implementation NAACL 2021 Haode Qi, Lin Pan, Atin Sood, Abhishek Shah, Ladislav Kunc, Mo Yu, Saloni Potdar

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.

Goal-Oriented Dialog Intent Detection

Towards building a Robust Industry-scale Question Answering System

no code implementations COLING 2020 Rishav Chakravarti, Anthony Ferritto, Bhavani Iyer, Lin Pan, Radu Florian, Salim Roukos, Avi Sil

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.

Data Augmentation Question Answering +1

Multilingual BERT Post-Pretraining Alignment

no code implementations NAACL 2021 Lin Pan, Chung-Wei Hang, Haode Qi, Abhishek Shah, Saloni Potdar, Mo Yu

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.

Contrastive Learning Language Modelling +1

ARES: A Reading Comprehension Ensembling Service

no code implementations EMNLP 2020 Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avi Sil

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.

Machine Reading Comprehension Question Answering

Ensembling Strategies for Answering Natural Questions

no code implementations30 Oct 2019 Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avirup Sil

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.

Question Answering

Frustratingly Easy Natural Question Answering

no code implementations11 Sep 2019 Lin Pan, Rishav Chakravarti, Anthony Ferritto, Michael Glass, Alfio Gliozzo, Salim Roukos, Radu Florian, Avirup Sil

Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa.

Data Augmentation Question Answering +1

Span Selection Pre-training for Question Answering

1 code implementation ACL 2020 Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, Avirup Sil

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).

Language Modelling Question Answering +1

CFO: A Framework for Building Production NLP Systems

no code implementations IJCNLP 2019 Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, Avirup Sil

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.

Information Retrieval Machine Reading Comprehension +1

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