International Business Machines Corporation

Ioannis Katsis

Xin Ru Wang

Douglas Ronald Burdick

Dulce Beatriz Ponceleon

Yunyao Li

Mustafa Canim

Shivakumar Vaithyanathan

Jaydeep Sen

Rajasekar Krishnamurthy

Saneem Ahmed Chemmengath

Samarth Bharadwaj

Vishwajeet Kumar

Christine T Wolf

Xinyi Zheng

A question answering bot that digests non-relational data tables is provided. A processor receives a question regarding a non-relational data table. A processor extracts at least one feature of the question using a natural language processing (NLP) model. A processor extracts at least one similar feature of the non-relational data table to the extracted at least one feature of the question. A processor determines at least one relevant cell in the non-relational data table based on the at least one feature of the question and the at least one similar feature of the non-relational data table. A processor provides an answer to the question, where the answer is based on the at least one relevant cell.

Natural language question answering using non-relational tables

Methods, systems, and computer program products for cooperative neural networks with spatial containment constraints are provided herein. A computer-implemented method includes dividing a processing task into multiple sub-tasks; training multiple independent neural networks, such that at least some of the multiple sub-tasks correspond to different ones of the multiple independent neural networks; defining, via implementing constraint-based domain knowledge related to the processing task in connection with the multiple independent neural networks, a constraint loss for a given one of the multiple sub-tasks, the constraint loss being dependent on output from at least one of the other multiple sub-tasks; and effecting re-training of at least a portion of the multiple independent neural networks by incorporating the constraint loss into at least one of the multiple independent neural networks.

Cooperative neural networks with spatial containment constraints

One embodiment provides a method, including: receiving two documents, one of the two documents having at least one table that includes the same information as a corresponding table in the other of the two documents, wherein (i) one of the two documents comprises the at least one table in an unstructured table representation and (ii) the other of the two documents comprises the at least one table in a structured table representation; identifying text elements within the at least one table in the unstructured table representation; matching the identified text elements with table elements within the at least one table in the structured table representation; and annotating the at least one table in the structured table representation based upon the matches between the table elements and text elements.

Automated non-native table representation annotation for machine-learning models

Embodiments are provided for analysis of errors of a predictive model. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an overview component that causes a client device to present first data identifying an error corresponding to a cell of a confusion matrix for a classification model, the error representing a mismatch between a first label generated by the classification model and a second label corresponding to a ground-truth observation. The computer-executable components also can include an element view component that receives second data defining a root cause of the error. The computer-executable components can further include an error annotation component that can embed the second data into a first data structure containing the first data, resulting in a first annotated data structure.

Error analysis of a predictive model

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San Jose, CA, United States of America

Ioannis Katsis