Search Results for author: Sida Wang

Found 18 papers, 6 papers with code

On Continual Model Refinement in Out-of-Distribution Data Streams

no code implementations ACL 2022 Bill Yuchen Lin, Sida Wang, Xi Victoria Lin, Robin Jia, Lin Xiao, Xiang Ren, Wen-tau Yih

Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrophic forgetting.

Continual Learning Natural Language Processing

InCoder: A Generative Model for Code Infilling and Synthesis

1 code implementation12 Apr 2022 Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis

Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming.

Program Synthesis

SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark

no code implementations NeurIPS 2021 Victor Zhong, Austin Hanjie, Sida Wang, Karthik Narasimhan, Luke Zettlemoyer

We hope SILG enables the community to quickly identify new methodolo- gies for language grounding that generalize to a diverse set of environments and their associated challenges.

Benchmark Grounded language learning +1

Deep Natural Language Processing for LinkedIn Search

no code implementations16 Aug 2021 Weiwei Guo, Xiaowei Liu, Sida Wang, Michaeel Kazi, Zhiwei Wang, Zhoutong Fu, Jun Jia, Liang Zhang, Huiji Gao, Bo Long

Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help.

Document Ranking Language Modelling +1

Deep Natural Language Processing for LinkedIn Search Systems

no code implementations30 Jul 2021 Weiwei Guo, Xiaowei Liu, Sida Wang, Michaeel Kazi, Zhoutong Fu, Huiji Gao, Jun Jia, Liang Zhang, Bo Long

Many search systems work with large amounts of natural language data, e. g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help.

Natural Language Processing

Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms

1 code implementation29 Dec 2020 Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad, Asish Ghoshal

Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process.

Active Learning

Efficient Neural Query Auto Completion

no code implementations6 Aug 2020 Sida Wang, Weiwei Guo, Huiji Gao, Bo Long

On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin.

Information Retrieval Language Modelling

Memory-efficient Embedding for Recommendations

no code implementations26 Jun 2020 Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long

Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.

AutoML Recommendation Systems

Pre-training via Paraphrasing

1 code implementation NeurIPS 2020 Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer

The objective noisily captures aspects of paraphrase, translation, multi-document summarization, and information retrieval, allowing for strong zero-shot performance on several tasks.

Document Summarization Document Translation +5

Simple MAP Inference via Low-Rank Relaxations

1 code implementation NeurIPS 2014 Roy Frostig, Sida Wang, Percy S. Liang, Christopher D. Manning

We focus on the problem of maximum a posteriori (MAP) inference in Markov random fields with binary variables and pairwise interactions.


Altitude Training: Strong Bounds for Single-Layer Dropout

no code implementations NeurIPS 2014 Stefan Wager, William Fithian, Sida Wang, Percy Liang

Dropout training, originally designed for deep neural networks, has been successful on high-dimensional single-layer natural language tasks.

Dropout Training as Adaptive Regularization

no code implementations NeurIPS 2013 Stefan Wager, Sida Wang, Percy Liang

Dropout and other feature noising schemes control overfitting by artificially corrupting the training data.

Document Classification online learning

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