Search Results for author: Abhishek Shah

Found 7 papers, 1 papers with code

A Hybrid Approach for Depression Classification: Random Forest-ANN Ensemble on Motor Activity Signals

no code implementations13 Oct 2023 Anket Patil, Dhairya Shah, Abhishek Shah, Mokshit Gala

Regarding the rising number of people suffering from mental health illnesses in today's society, the importance of mental health cannot be overstated.

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.

Benchmarking Goal-Oriented Dialog +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 +2

Iterative Data Programming for Expanding Text Classification Corpora

no code implementations4 Feb 2020 Neil Mallinar, Abhishek Shah, Tin Kam Ho, Rajendra Ugrani, Ayush Gupta

Real-world text classification tasks often require many labeled training examples that are expensive to obtain.

Denoising Ensemble Learning +5

Fine Grained Dataflow Tracking with Proximal Gradients

no code implementations8 Sep 2019 Gabriel Ryan, Abhishek Shah, Dongdong She, Koustubha Bhat, Suman Jana

Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel information leak detection.

Cryptography and Security

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

no code implementations ACL 2019 Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, Miguel Ballesteros

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs.

AMR Parsing reinforcement-learning +1

Bootstrapping Conversational Agents With Weak Supervision

no code implementations14 Dec 2018 Neil Mallinar, Abhishek Shah, Rajendra Ugrani, Ayush Gupta, Manikandan Gurusankar, Tin Kam Ho, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, Robert Yates, Chris Desmarais, Blake McGregor

We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling.

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