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Found 36 papers, 12 papers with code
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations
While these tasks partially evaluate machines’ ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning.
A Meta-framework for Spatiotemporal Quantity Extraction from Text
News events are often associated with quantities (e. g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events.
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments, and propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints.
PInKS: Preconditioned Commonsense Inference with Minimal Supervision
Reasoning with preconditions such as "glass can be used for drinking water unless the glass is shattered" remains an open problem for language models.
Answer Consolidation: Formulation and Benchmarking
Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer.
Event-Centric Natural Language Processing
This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text.
SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM).
Event Time Extraction and Propagation via Graph Attention Networks
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
Extracting Temporal Event Relation with Syntax-guided Graph Transformer
Extracting temporal relations (e. g., before, after, and simultaneous) among events is crucial to natural language understanding.
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning
While these tasks partially evaluate machines' ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning.
SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM).
PABI: A Unified PAC-Bayesian Informativeness Measure for Incidental Supervision Signals
Real-world applications often require making use of {\em a range of incidental supervision signals}.
Temporal Reasoning on Implicit Events from Distant Supervision
We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural language text but can be inferred from it.
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ
High-quality and large-scale data are key to success for AI systems.
Evaluating NLP Models via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
Learnability with Indirect Supervision Signals
Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly.
Foreseeing the Benefits of Incidental Supervision
Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.
Temporal Common Sense Acquisition with Minimal Supervision
Temporal common sense (e. g., duration and frequency of events) is crucial for understanding natural language.
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated.
Ranked #2 on
Question Answering
on Torque
Evaluating Models' Local Decision Boundaries via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
``Going on a vacation'' takes longer than ``Going for a walk'': A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural language.
KnowSemLM: A Knowledge Infused Semantic Language Model
Story understanding requires developing expectations of what events come next in text.
"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural language.
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction
We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction.
Event Extraction
Joint Event and Temporal Relation Extraction
+4
An Improved Neural Baseline for Temporal Relation Extraction
Determining temporal relations (e. g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data.
QuASE: Question-Answer Driven Sentence Encoding
Question-answering (QA) data often encodes essential information in many facets.
CogCompTime: A Tool for Understanding Time in Natural Language Text
Automatic extraction of temporal information in text is an important component of natural language understanding.
Partial Or Complete, That's The Question
For many structured learning tasks, the data annotation process is complex and costly.
A Structured Learning Approach to Temporal Relation Extraction
Identifying temporal relations between events is an essential step towards natural language understanding.
Ranked #1 on
Temporal Information Extraction
on TempEval-3
Joint Reasoning for Temporal and Causal Relations
Understanding temporal and causal relations between events is a fundamental natural language understanding task.
CogCompTime: A Tool for Understanding Time in Natural Language
Automatic extraction of temporal information is important for natural language understanding.
Exploiting Partially Annotated Data in Temporal Relation Extraction
As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.
A Multi-Axis Annotation Scheme for Event Temporal Relations
Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition.
Exploiting Partially Annotated Data for Temporal Relation Extraction
As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow.
