Search Results for author:
Found 11 papers, 3 papers with code
Contextual Rephrase Detection for Reducing Friction in Dialogue Systems
Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e. g. users’ implicit feedback).
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering
However, a large amount of world's knowledge is stored in structured databases, and need to be accessed using query languages such as SQL.
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).
Ranked #3 on
Semantic Parsing
on spider
Decomposed Adversarial Learned Inference
Effective inference for a generative adversarial model remains an important and challenging problem.
Censored Quantile Regression Forest
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases.
Semi-Supervised Learning for Text Classification by Layer Partitioning
In this way, $F$ serves as a feature extractor that maps the input to high-level representation and adds systematical noise using dropout.
Knowledge Enhanced Attention for Robust Natural Language Inference
Neural network models have been very successful at achieving high accuracy on natural language inference (NLI) tasks.
Censored Quantile Regression Forests
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases.
Forest-type Regression with General Losses and Robust Forest
This paper introduces a new general framework for forest-type regression which allows the development of robust forest regressors by selecting from a large family of robust loss functions.
Boosting in the presence of outliers: adaptive classification with non-convex loss functions
Along with the Arch Boosting framework, the non-convex losses lead to the new class of boosting algorithms, named adaptive, robust, boosting (ARB).
