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.
Ranked #3 on Dialog Relation Extraction on DialogRE
To gain insights into the reasoning process of a generation model, we propose anew method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input and output sentences.
In this paper, we introduce HyperFair, a general framework for enforcing soft fairness constraints in a hybrid recommender system.
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.
However, this task is challenging as the variational attributes are often present as a part of unstructured text and are domain dependent.
In this paper, we study the effectiveness of these approaches in estimating aggregate properties on networks with missing attributes.
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.
1 code implementation • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
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.
Knowledge graph (KG) embedding techniques use structured relationships between entities to learn low-dimensional representations of entities and relations.
In this appendix we provide additional supplementary material to "A Collective, Probabilistic Approach to Schema Mapping."
A common theme in this research has been the importance of incorporating relational features into the resolution process.
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.
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.
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.
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.
In modern data science problems, techniques for extracting value from big data require performing large-scale optimization over heterogenous, irregularly structured data.
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.
For the typical cases of real-valued functions and binary relations, we propose several loss functions and derive the associated parameter gradients.
We investigate the generalizability of learned binary relations: functions that map pairs of instances to a logical indicator.
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.
Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes.