Search Results for author:
Found 18 papers, 6 papers with code
Zero-shot Entity Linking with Less Data
Interestingly, we discovered that BLINK exhibits diminishing returns, i. e., it reaches 98% of its performance with just 1% of the training data and the remaining 99% of the data yields only a marginal increase of 2% in the performance.
Read between the lines -- Functionality Extraction From READMEs
While text summarization is a well-known NLP task, in this paper, we introduce a novel and useful variant of it called functionality extraction from Git README files.
Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word Problems
Utilizing the specific format of this task, we propose three novel techniques that improve performance: Rephrase reformulates the given problem into a forward reasoning problem, PAL-Tools combines the idea of Program-Aided LLMs to produce a set of equations that can be solved by an external solver, and Check your Work exploits the availability of natural verifier of high accuracy in the forward direction, interleaving solving and verification steps.
Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach
We are interested in image manipulation via natural language text -- a task that is useful for multiple AI applications but requires complex reasoning over multi-modal spaces.
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases
In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper.
Knowledge Graph Question Answering via SPARQL Silhouette Generation
Experimental results show that the quality of generated SPARQL silhouette in the first stage is outstanding for the ideal scenarios but for realistic scenarios (i. e. noisy linker), the quality of the resulting SPARQL silhouette drops drastically.
Explanations for CommonsenseQA: New Dataset and Models
We human-annotate a first-of-its-kind dataset (called ECQA) of positive and negative properties, as well as free-flow explanations, for $11K$ QA pairs taken from the CQA dataset.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Inductive Quantum Embedding
Our training runs 9-times faster than the original QE scheme on this task.
Quantum Embedding of Knowledge for Reasoning
Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs).
The TechQA Dataset
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
Span Selection Pre-training for Question Answering
BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA).
Deep Domain Adaptation under Deep Label Scarcity
The goal behind Domain Adaptation (DA) is to leverage the labeled examples from a source domain so as to infer an accurate model in a target domain where labels are not available or in scarce at the best.
Improved Linear Embeddings via Lagrange Duality
Near isometric orthogonal embeddings to lower dimensions are a fundamental tool in data science and machine learning.
Latent Space Embedding for Retrieval in Question-Answer Archives
The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval.
A Sparse Nonlinear Classifier Design Using AUC Optimization
Batch learning methods for solving the kernelized version of this problem suffer from scalability and may not result in sparse classifiers.
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds
We study the problem of training an accurate linear regression model by procuring labels from multiple noisy crowd annotators, under a budget constraint.
