Reasoning about Actions and State Changes by Injecting Commonsense Knowledge

@inproceedings{Tandon2018ReasoningAA,
  title={Reasoning about Actions and State Changes by Injecting Commonsense Knowledge},
  author={Niket Tandon and Bhavana Dalvi and Joel Grus and Wen-tau Yih and Antoine Bosselut and Peter Clark},
  booktitle={Conference on Empirical Methods in Natural Language Processing},
  year={2018}
}
Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered. [] Key Method Unlike earlier methods, we treat the problem as a neural structured prediction task, allowing hard and soft constraints to steer the model away from unlikely predictions. We show that the new model significantly outperforms earlier systems on a benchmark dataset for procedural text…
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References

SHOWING 1-10 OF 24 REFERENCES

What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text

This work leverages VerbNet to build a rulebase of the preconditions and effects of actions, and uses it along with commonsense knowledge of persistence to answer questions about change in paragraphs describing processes.

Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension

A new dataset and models for comprehending paragraphs about processes, an important genre of text describing a dynamic world, are presented and two new neural models that exploit alternative mechanisms for state prediction are introduced, in particular using LSTM input encoding and span prediction.

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

This work argues for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering, and classify these tasks into skill sets so that researchers can identify (and then rectify) the failings of their systems.

Mise en Place: Unsupervised Interpretation of Instructional Recipes

An unsupervised hard EM approach to automatically mapping instructional recipes to action graphs, which define what actions should be performed on which objects and in what order, which incorporates aspects of procedural semantics and world knowledge.

Simulating Action Dynamics with Neural Process Networks

This work introduces Neural Process Networks to understand procedural text through (neural) simulation of action dynamics, and complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers.

Tracking the World State with Recurrent Entity Networks

The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting, and can generalize past its training horizon.

Bidirectional Attention Flow for Machine Comprehension

The BIDAF network is introduced, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization.

Query-Reduction Networks for Question Answering

Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term and long-term sequential dependencies to reason over multiple facts, is proposed.

Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

A new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables with a state-of-the-art accuracy and type constraints and entity linking are valuable components to incorporate in neural semantic parsers.

Modeling Biological Processes for Reading Comprehension

This paper focuses on a new reading comprehension task that requires complex reasoning over a single document, and demonstrates that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.