ARISTO

Build machines that read, learn and reason.

The Aristo Project aims to build systems that demonstrate a deep understanding of the world, integrating technologies for reading, learning, reasoning, and explanation.

A multiple choice question and reasoning explaining each answer

Our research integrates multiple AI technologies, including:

Natural language processing
Information extraction
Knowledge representation
Machine reasoning
Commonsense knowledge

Research Areas

Probing Reasoning with Language Models

Language models (LMs) have dominated much of AI recently. But what kind(s) of reasoning are they capable of? And how can they be taught to do more? We are developing analytical datasets to probe LMs and help answer these questions.

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Probing Natural Language Inference Models through Semantic Fragments

Multihop Reasoning

Many questions require multiple pieces of information to be combined to arrive at an answer. We are developing new multihop models capable of identifying and combining relevant facts to answer such questions.

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Repurposing Entailment for Multi-Hop Question Answering Tasks

QASC: A Dataset for Question Answering via Sentence Composition

Explanation

An intelligent system should not only answer questions correctly, but also be able to explain why its answers are correct. Such a capability is essential for practical acceptance of AI technology. It is also essential for the broader goals of communicating knowledge to a user, and receiving correction from the user when the system's answer is wrong.

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What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering

Exploiting Explicit Paths for Multi-hop Reading Comprehension

Reasoning about Actions

A key aspect of intelligence is being able to reason about the dynamics of the world. This requires modeling what state the world might be in, and how different actions might affect that state. Such capabilities are essential for understanding what happens during a procedure or process, for planning, and for reasoning about "what if..." scenarios.

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Reasoning about Actions and State Changes by Injecting Commonsense Knowledge

Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text

Demos

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RuleTaker

Transformers as Soft Reasoners over Language | Aristo

RuleTaker determines whether statements are True or False based on rules given in natural language.

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RuleTaker

Transformers as Soft Reasoners over Language | Aristo

RuleTaker determines whether statements are True or False based on rules given in natural language.

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UnifiedQA

Crossing format boundaries with a single QA system | Aristo

UnifiedQA is a single pre-trained QA model that performs surprisingly well across 17 QA datasets spanning 4 diverse formats. Fine-tuning UnifiedQA into specialized models results in a new state-of-the-art on 6 datasets, establishing this model as a strong starting point for building QA systems.

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UnifiedQA

Crossing format boundaries with a single QA system | Aristo

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Recent Papers

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Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text
Bhavana Dalvi Mishra, Niket Tandon, Antoine Bosselut, Wen-tau Yih, Peter ClarkEMNLP • 2019Our goal is to better comprehend procedural text, e.g., a paragraph about photosynthesis, by not only predicting what happens, but why some actions need to happen before others. Our approach builds on a prior process comprehension framework for predicting actions' effects, to also identify… more
QASC: A Dataset for Question Answering via Sentence Composition
Tushar Khot, Peter Clark, Michal Guerquin, Paul Edward Jansen, Ashish Sabharwal AAAI • 2020Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice… more
Probing Natural Language Inference Models through Semantic Fragments
Kyle Richardson, Hai Na Hu, Lawrence S. Moss, Ashish SabharwalAAAI • 2020Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about word substitutions in sentential contexts)? While such phenomena are… more
Modular Representation Underlies Systematic Generalization in Neural Natural Language Inference Models
Atticus Geiger, Kyle Richardson, Christopher PottsBlackbox NLP • 2020In adversarial (challenge) testing, we pose hard generalization tasks in order to gain insights into the solutions found by our models. What properties must a system have in order to succeed at these hard tasks? In this paper, we argue that an essential factor is the ability to form modular… more
A Simple Yet Strong Pipeline for HotpotQA
Dirk Groeneveld, Tushar Khot, Mausam, Ashish SabharwalEMNLP • 2020State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition. However, does their strong performance on popular… more