Search Results for author: Lise Getoor

Found 36 papers, 8 papers with code

Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic

no code implementations26 Mar 2024 Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor

Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i. e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog.

Domain Generalization Few-Shot Learning +1

Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning

2 code implementations17 Jan 2024 Charles Dickens, Changyu Gao, Connor Pryor, Stephen Wright, Lise Getoor

We address a key challenge for neuro-symbolic (NeSy) systems by leveraging convex and bilevel optimization techniques to develop a general gradient-based framework for end-to-end neural and symbolic parameter learning.

Bilevel Optimization

ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation

no code implementations30 Jan 2023 Kaiwen Zhou, Kaizhi Zheng, Connor Pryor, Yilin Shen, Hongxia Jin, Lise Getoor, Xin Eric Wang

Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments.

Efficient Exploration Language Modelling +2

CausalDialogue: Modeling Utterance-level Causality in Conversations

1 code implementation20 Dec 2022 Yi-Lin Tuan, Alon Albalak, Wenda Xu, Michael Saxon, Connor Pryor, Lise Getoor, William Yang Wang

Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.

Dialogue Generation

Emotion Recognition in Conversation using Probabilistic Soft Logic

no code implementations14 Jul 2022 Eriq Augustine, Pegah Jandaghi, Alon Albalak, Connor Pryor, Charles Dickens, William Wang, Lise Getoor

Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.

Emotion Recognition in Conversation Logical Reasoning +2

NeuPSL: Neural Probabilistic Soft Logic

1 code implementation27 May 2022 Connor Pryor, Charles Dickens, Eriq Augustine, Alon Albalak, William Wang, Lise Getoor

In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks.

FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue

1 code implementation12 May 2022 Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang

Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.

Dialogue Understanding Domain Adaptation +1

D-REX: Dialogue Relation Extraction with Explanations

1 code implementation NLP4ConvAI (ACL) 2022 Alon Albalak, Varun Embar, Yi-Lin Tuan, Lise Getoor, William Yang Wang

Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods.

Dialog Relation Extraction Relation +3

Local Explanation of Dialogue Response Generation

1 code implementation NeurIPS 2021 Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang

To gain insights into the reasoning process of a generation model, we propose a new method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input and output sentences.

Implicit Relations Response Generation +1

HyperFair: A Soft Approach to Integrating Fairness Criteria

no code implementations5 Sep 2020 Charles Dickens, Rishika Singh, Lise Getoor

In this paper, we introduce HyperFair, a general framework for enforcing soft fairness constraints in a hybrid recommender system.

Fairness Recommendation Systems

Causal Relational Learning

no code implementations7 Apr 2020 Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.

Causal Inference Decision Making +1

Estimating Aggregate Properties In Relational Networks With Unobserved Data

no code implementations16 Jan 2020 Varun Embar, Sriram Srinivasan, Lise Getoor

In this paper, we study the effectiveness of these approaches in estimating aggregate properties on networks with missing attributes.

Relational Reasoning

User Profiling Using Hinge-loss Markov Random Fields

no code implementations5 Jan 2020 Golnoosh Farnadi, Lise Getoor, Marie-Francine Moens, Martine De Cock

In this paper, we propose a mechanism to infer a variety of user characteristics, such as, age, gender and personality traits, which can then be compiled into a user profile.

Relational Reasoning

Estimating Causal Effects of Tone in Online Debates

1 code implementation10 Jun 2019 Dhanya Sridhar, Lise Getoor

In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses.


Scalable Structure Learning for Probabilistic Soft Logic

no code implementations3 Jul 2018 Varun Embar, Dhanya Sridhar, Golnoosh Farnadi, Lise Getoor

We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways.

Using Noisy Extractions to Discover Causal Knowledge

no code implementations16 Nov 2017 Dhanya Sridhar, Jay Pujara, Lise Getoor

Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning.

Causal Discovery

Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short

1 code implementation EMNLP 2017 Jay Pujara, Eriq Augustine, Lise Getoor

Knowledge graph (KG) embedding techniques use structured relationships between entities to learn low-dimensional representations of entities and relations.

Knowledge Graph Embeddings Knowledge Graphs +1

A Collective, Probabilistic Approach to Schema Mapping: Appendix

no code implementations11 Feb 2017 Angelika Kimmig, Alex Memory, Renee J. Miller, Lise Getoor

In this appendix we provide additional supplementary material to "A Collective, Probabilistic Approach to Schema Mapping."

Generic Statistical Relational Entity Resolution in Knowledge Graphs

no code implementations4 Jul 2016 Jay Pujara, Lise Getoor

A common theme in this research has been the importance of incorporating relational features into the resolution process.

Entity Resolution Knowledge Graphs

Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks

no code implementations2 Jul 2016 Shobeir Fakhraei, Dhanya Sridhar, Jay Pujara, Lise Getoor

A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction.

graph construction Link Prediction

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

no code implementations17 May 2015 Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor

In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data.

Knowledge Graphs Probabilistic Programming

Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition

no code implementations16 Jan 2014 Mustafa Bilgic, Lise Getoor

We address the cost-sensitive feature acquisition problem, where misclassifying an instance is costly but the expected misclassification cost can be reduced by acquiring the values of the missing features.

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction

no code implementations26 Sep 2013 Stephen Bach, Bert Huang, Ben London, Lise Getoor

Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable.

Structured Prediction

A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization

no code implementations30 Aug 2013 Hui Miao, Xiangyang Liu, Bert Huang, Lise Getoor

In modern data science problems, techniques for extracting value from big data require performing large-scale optimization over heterogenous, irregularly structured data.

hypergraph partitioning

Scalable Text and Link Analysis with Mixed-Topic Link Models

no code implementations28 Mar 2013 Yaojia Zhu, Xiaoran Yan, Lise Getoor, Cristopher Moore

The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm.

Link Prediction Topic Classification

Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss

no code implementations7 Mar 2013 Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor

For the typical cases of real-valued functions and binary relations, we propose several loss functions and derive the associated parameter gradients.

Relational Reasoning Tensor Decomposition

Graph-based Generalization Bounds for Learning Binary Relations

no code implementations21 Feb 2013 Ben London, Bert Huang, Lise Getoor

We investigate the generalizability of learned binary relations: functions that map pairs of instances to a logical indicator.

Entity Resolution Generalization Bounds +1

Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization

no code implementations NeurIPS 2012 Stephen Bach, Matthias Broecheler, Lise Getoor, Dianne O'Leary

In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains.

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