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Found 30 papers, 12 papers with code
A Systematic Survey of Text Worlds as Embodied Natural Language Environments
Text Worlds are virtual environments for embodied agents that, unlike 2D or 3D environments, are rendered exclusively using textual descriptions.
TextGraphs 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration
In this second iteration of the explanation regeneration shared task, participants are supplied with more than double the training and evaluation data of the first shared task, as well as a knowledge base nearly double in size, both of which expand into more challenging scientific topics that increase the difficulty of the task.
TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration
While previous editions of this shared task aimed to evaluate explanatory completeness – finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations.
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Contemporary language models enable new opportunities for structured reasoning with text, such as the construction and evaluation of intuitive, proof-like textual entailment trees without relying on brittle formal logic.
Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games
In this work, we introduce a self-supervised behavior cloning transformer for text games, which are challenging benchmarks for multi-step reasoning in virtual environments.
CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
Language agents have shown some ability to interact with an external environment, e. g., a virtual world such as ScienceWorld, to perform complex tasks, e. g., growing a plant, without the startup costs of reinforcement learning.
From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions
In this work, we show that contemporary language models have a previously unknown skill -- the capacity for electronic circuit design from high-level textual descriptions, akin to code generation.
ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games
In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks.
Behavior Cloned Transformers are Neurosymbolic Reasoners
In this work, we explore techniques for augmenting interactive agents with information from symbolic modules, much like humans use tools like calculators and GPS systems to assist with arithmetic and navigation.
ScienceWorld: Is your Agent Smarter than a 5th Grader?
We present ScienceWorld, a benchmark to test agents' scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum.
Extracting Space Situational Awareness Events from News Text
Space situational awareness typically makes use of physical measurements from radar, telescopes, and other assets to monitor satellites and other spacecraft for operational, navigational, and defense purposes.
On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings
Building compositional explanations requires models to combine two or more facts that, together, describe why the answer to a question is correct.
Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed Language
Tamarian, a fictional language introduced in the Star Trek episode Darmok, communicates meaning through utterances of metaphorical references, such as "Darmok and Jalad at Tanagra" instead of "We should work together."
Explaining Answers with Entailment Trees
Our approach is to generate explanations in the form of entailment trees, namely a tree of multipremise entailment steps from facts that are known, through intermediate conclusions, to the hypothesis of interest (namely the question + answer).
Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions
The recently proposed ALFRED challenge task aims for a virtual robotic agent to complete complex multi-step everyday tasks in a virtual home environment from high-level natural language directives, such as {``}put a hot piece of bread on a plate{''}.
CoSaTa: A Constraint Satisfaction Solver and Interpreted Language for Semi-Structured Tables of Sentences
This work presents CoSaTa, an intuitive constraint satisfaction solver and interpreted language for knowledge bases of semi-structured tables expressed as text.
WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference
Explainable question answering for complex questions often requires combining large numbers of facts to answer a question while providing a human-readable explanation for the answer, a process known as multi-hop inference.
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks.
Extracting Common Inference Patterns from Semi-Structured Explanations
Complex questions often require combining multiple facts to correctly answer, particularly when generating detailed explanations for why those answers are correct.
TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration
While automated question answering systems are increasingly able to retrieve answers to natural language questions, their ability to generate detailed human-readable explanations for their answers is still quite limited.
QASC: A Dataset for Question Answering via Sentence Composition
Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges.
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams
Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately.
Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?
Question Answering for complex questions is often modeled as a graph construction or traversal task, where a solver must build or traverse a graph of facts that answer and explain a given question.
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification
We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection.
Ranked #1 on
Question Answering
on AI2 Kaggle Dataset
Framing QA as Building and Ranking Intersentence Answer Justifications
Our best configuration answers 44{\%} of the questions correctly, where the top justifications for 57{\%} of these correct answers contain a compelling human-readable justification that explains the inference required to arrive at the correct answer.
What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
These explanations are used to create a fine-grained categorization of the requirements.
Creating Causal Embeddings for Question Answering with Minimal Supervision
We argue that a better approach is to look for answers that are related to the question in a relevant way, according to the information need of the question, which may be determined through task-specific embeddings.
Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
We introduce a higher-order formalism that allows all these lexical semantic models to chain direct evidence to construct indirect associations between question and answer texts, by casting the task as the traversal of graphs that encode direct term associations.
