Search Results for author:
Found 12 papers, 3 papers with code
NL-EDIT: Correcting semantic parse errors through natural language interaction
We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors.
It Takes Two to Lie: One to Lie, and One to Listen
Trust is implicit in many online text conversations{---}striking up new friendships, or asking for tech support.
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback
We study the task of semantic parse correction with natural language feedback.
Can You Unpack That? Learning to Rewrite Questions-in-Context
Question answering is an AI-complete problem, but existing datasets lack key elements of language understanding such as coreference and ellipsis resolution.
A dataset and baselines for sequential open-domain question answering
Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context.
Assessing Composition in Sentence Vector Representations
We describe the details of the method and generation system, and then present results of experiments applying our method to probe for compositional information in embeddings from a number of existing sentence composition models.
Construction of the Literature Graph in Semantic Scholar
We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery.
Generating Natural Language Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify.
Greedy Column Subset Selection for Large-scale Data Sets
The algorithm first learns a concise representation of all columns using random projection, and it then solves a generalized column subset selection problem at each machine in which a subset of columns are selected from the sub-matrix on that machine such that the reconstruction error of the concise representation is minimized.
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce
Exploiting the proposed parallelization strategy, we present two scalable MapReduce algorithms for kernel $k$-means.
