Search Results for author: Saloni Potdar

Found 12 papers, 6 papers with code

Improved Text Classification via Contrastive Adversarial Training

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

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

Classification Contrastive Learning +3

Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study

3 code implementations7 Jun 2021 Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su

Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.

Open-Domain Question Answering

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 +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

Frustratingly Hard Evidence Retrieval for QA Over Books

no code implementations WS 2020 Xiangyang Mou, Mo Yu, Bingsheng Yao, Chenghao Yang, Xiaoxiao Guo, Saloni Potdar, Hui Su

A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.

Question Answering

Context-Aware Conversation Thread Detection in Multi-Party Chat

no code implementations IJCNLP 2019 Ming Tan, Dakuo Wang, Yupeng Gao, Haoyu Wang, Saloni Potdar, Xiaoxiao Guo, Shiyu Chang, Mo Yu

In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.

Robust Task Clustering for Deep Many-Task Learning

no code implementations26 Aug 2017 Mo Yu, Xiaoxiao Guo, Jin-Feng Yi, Shiyu Chang, Saloni Potdar, Gerald Tesauro, Haoyu Wang, Bo-Wen Zhou

We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario.

Few-Shot Learning General Classification +4

Neural Models for Sequence Chunking

1 code implementation15 Jan 2017 Feifei Zhai, Saloni Potdar, Bing Xiang, Bo-Wen Zhou

Many natural language understanding (NLU) tasks, such as shallow parsing (i. e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence.

Chunking Natural Language Understanding +1

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