Search Results for author:
Found 19 papers, 8 papers with code
How Does BERT Rerank Passages? An Attribution Analysis with Information Bottlenecks
Fine-tuned pre-trained transformers achieve the state of the art in passage reranking.
Bag-of-Words Baselines for Semantic Code Search
The task of semantic code search is to retrieve code snippets from a source code corpus based on an information need expressed in natural language.
Voice Query Auto Completion
Query auto completion (QAC) is the task of predicting a search engine user’s final query from their intermediate, incomplete query.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Early Exiting BERT for Efficient Document Ranking
Pre-trained language models such as BERT have shown their effectiveness in various tasks.
Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval
Recent work has shown that dense passage retrieval techniques achieve better ranking accuracy in open-domain question answering compared to sparse retrieval techniques such as BM25, but at the cost of large space and memory requirements.
MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting
Transformer-based models have greatly pushed the boundaries of time series forecasting recently.
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers
There exists a wide variety of efficiency methods for natural language processing (NLP) tasks, such as pruning, distillation, dynamic inference, quantization, etc.
Few-Shot Non-Parametric Learning with Deep Latent Variable Model
Most real-world problems that machine learning algorithms are expected to solve face the situation with 1) unknown data distribution; 2) little domain-specific knowledge; and 3) datasets with limited annotation.
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking
For example, on MS MARCO Passage v1, our method yields an average candidate set size of 27 out of 1, 000 which increases the reranking speed by about 37 times, while the MRR@10 is greater than a pre-specified value of 0. 38 with about 90% empirical coverage and the empirical baselines fail to provide such guarantee.
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations
Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search.
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representation
Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search.
The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing
To fill this void in the literature, we study in this paper selective prediction for NLP, comparing different models and confidence estimators.
BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression
The slow speed of BERT has motivated much research on accelerating its inference, and the early exiting idea has been proposed to make trade-offs between model quality and efficiency.
Inserting Information Bottlenecks for Attribution in Transformers
We show the effectiveness of our method in terms of attribution and the ability to provide insight into how information flows through layers.
Showing Your Work Doesn't Always Work
In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks.
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
Large-scale pre-trained language models such as BERT have brought significant improvements to NLP applications.
Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits
Public vulnerability databases such as CVE and NVD account for only 60% of security vulnerabilities present in open-source projects, and are known to suffer from inconsistent quality.
What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons
Memory neurons of long short-term memory (LSTM) networks encode and process information in powerful yet mysterious ways.
Put It Back: Entity Typing with Language Model Enhancement
Entity typing aims to classify semantic types of an entity mention in a specific context.
