Search Results for author:
Found 35 papers, 20 papers with code
Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference.
Entity Linking via Explicit Mention-Mention Coreference Modeling
Learning representations of entity mentions is a core component of modern entity linking systems for both candidate generation and making linking predictions.
Ranked #1 on
Entity Linking
on ZESHEL
Knowledge Informed Semantic Parsing for Conversational Question Answering
Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers.
SIKeD: Self-guided Iterative Knowledge Distillation for mathematical reasoning
While LLMs can accurately solve reasoning tasks through a variety of strategies, even without fine-tuning, smaller models are not expressive enough to fit the LLMs distribution on all strategies when distilled and tend to prioritize one strategy over the others.
A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks
In dense retrieval, deep encoders provide embeddings for both inputs and targets, and the softmax function is used to parameterize a distribution over a large number of candidate targets (e. g., textual passages for information retrieval).
Analysis of Plan-based Retrieval for Grounded Text Generation
In text generation, hallucinations refer to the generation of seemingly coherent text that contradicts established knowledge.
Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders
Our method produces a high-quality approximation while requiring only a fraction of CE calls as compared to CUR-based methods, and allows for leveraging DE to initialize the embedding space while avoiding compute- and resource-intensive finetuning of DE via distillation.
Incremental Extractive Opinion Summarization Using Cover Trees
In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time.
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Distributed representations provide a vector space that captures meaningful relationships between data instances.
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
In the online setting, where the algorithm has access to a single instance at a time, estimating the group fairness objective requires additional storage and significantly more computation (e. g., forward/backward passes) than the task-specific objective at every time step.
Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR Decomposition
While ANNCUR's one-time selection of anchors tends to approximate the cross-encoder distances on average, doing so forfeits the capacity to accurately estimate distances to items near the query, leading to regret in the crucial end-task: recall of top-k items.
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining
First, we introduce an algorithm that uses a tree structure to approximate the softmax with provable bounds and that dynamically maintains the tree.
Autoregressive Structured Prediction with Language Models
Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks.
Ranked #1 on
Relation Extraction
on CoNLL04
(NER Micro F1 metric)
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
When the similarity is measured by dot-product between dual-encoder vectors or $\ell_2$-distance, there already exist many scalable and efficient search methods.
Longtonotes: OntoNotes with Longer Coreference Chains
Ontonotes has served as the most important benchmark for coreference resolution.
Unsupervised Opinion Summarization Using Approximate Geodesics
We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism.
Sublinear Time Approximation of Text Similarity Matrices
Approximation methods reduce this quadratic complexity, often by using a small subset of exactly computed similarities to approximate the remainder of the complete pairwise similarity matrix.
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
While vectors in Euclidean space can theoretically represent any graph, much recent work shows that alternatives such as complex, hyperbolic, order, or box embeddings have geometric properties better suited to modeling real-world graphs.
Entity Linking and Discovery via Arborescence-based Supervised Clustering
Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions.
Exact and Approximate Hierarchical Clustering Using A*
In those cases, hierarchical clustering can be seen as a combinatorial optimization problem.
Model-Agnostic Graph Regularization for Few-Shot Learning
In this paper, we present a comprehensive empirical study on graph embedded few-shot learning.
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
Clustering-based Inference for Biomedical Entity Linking
In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions.
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem.
Ranked #1 on
Link Prediction
on NELL-995
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
In this paper, we describe a technique for using BibTeX to generate, automatically, a large-scale 41M labeled strings), labeled dataset, that is four orders of magnitude larger than the current largest CFE dataset, namely the UMass Citation Field Extraction dataset [Anzaroot and McCallum, 2013].
Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
In contrast to existing methods, we present novel dynamic-programming algorithms for \emph{exact} inference in hierarchical clustering based on a novel trellis data structure, and we prove that we can exactly compute the partition function, maximum likelihood hierarchy, and marginal probabilities of sub-hierarchies and clusters.
Predicting Institution Hierarchies with Set-based Models
The hierarchical structure of research organizations plays a pivotal role in science of science research as well as in tools that track the research achievements and output.
Scalable Hierarchical Clustering with Tree Grafting
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets.
Optimal Transport-based Alignment of Learned Character Representations for String Similarity
We evaluate STANCE's ability to detect whether two strings can refer to the same entity--a task we term alias detection.
Supervised Hierarchical Clustering with Exponential Linkage
Thus we introduce an approach to supervised hierarchical clustering that smoothly interpolates between single, average, and complete linkage, and we give a training procedure that simultaneously learns a linkage function and a dissimilarity function.
Compact Representation of Uncertainty in Clustering
For many classic structured prediction problems, probability distributions over the dependent variables can be efficiently computed using widely-known algorithms and data structures (such as forward-backward, and its corresponding trellis for exact probability distributions in Markov models).
Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction
Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations.
Play Duration based User-Entity Affinity Modeling in Spoken Dialog System
We first binarize play durations to obtain implicit positive and negative affinity labels.
An Online Hierarchical Algorithm for Extreme Clustering
Many modern clustering methods scale well to a large number of data items, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K--a problem setting we term extreme clustering.
