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Found 21 papers, 2 papers with code
Text Counterfactuals via Latent Optimization and Shapley-Guided Search
We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification.
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions
This paper investigates different variants of PointConv, a convolution network on point clouds, to examine their robustness to input scale and rotation changes.
Relation Extraction with Explanation
Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences.
Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
We present a new local entity disambiguation system.
Saliency Learning: Teaching the Model Where to Pay Attention
Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances.
Attentional Multi-Reading Sarcasm Detection
We also study the behavior of the proposed model to provide explanations for the model's decisions.
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Deep learning models have achieved remarkable success in natural language inference (NLI) tasks.
Joint Neural Entity Disambiguation with Output Space Search
In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS).
Dependent Gated Reading for Cloze-Style Question Answering
We present a novel deep learning architecture to address the cloze-style question answering task.
Event Nugget Detection with Forward-Backward Recurrent Neural Networks
Traditional event detection methods heavily rely on manually engineered rich features.
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.
Ranked #16 on
Natural Language Inference
on SNLI
Weakly-supervised Dictionary Learning
The performance of the proposed model is demonstrated on both synthetic and real-world data.
Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data
In this approach each bag is represented as a distribution using the principle of ME.
Discriminative Clustering with Relative Constraints
Experimental results demonstrate: 1) the usefulness of relative constraints, in particular when don't know answers are considered; 2) the improved performance of the proposed method over state-of-the-art methods that utilize either relative or pairwise constraints; and 3) the robustness of our method in the presence of noisy constraints, such as those provided by human judgement.
Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning
To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label.
Active Metric Learning from Relative Comparisons
This work focuses on active learning of distance metrics from relative comparison information.
Novelty Detection Under Multi-Instance Multi-Label Framework
Contrary to the common assumption in MIML that each instance in a bag belongs to one of the known classes, in novelty detection, we focus on the scenario where bags may contain novel-class instances.
Multi-Label Classifier Chains for Bird Sound
Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of different species.
Budgeted Optimization with Concurrent Stochastic-Duration Experiments
This paper defines a novel problem formulation with the following important extensions: 1) allowing for concurrent experiments; 2) allowing for stochastic experiment durations; and 3) placing constraints on both the total number of experiments and the total experimental time.
Inverting Grice's Maxims to Learn Rules from Natural Language Extractions
However, unlike standard approaches to missing data, in this setting we know that facts are more likely to be missing from the text in cases where the reader can infer them from the facts that are mentioned combined with the domain knowledge.
Batch Bayesian Optimization via Simulation Matching
Bayesian optimization methods are often used to optimize unknown functions that are costly to evaluate.
