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Found 26 papers, 9 papers with code
MegaWika: Millions of reports and their sources across 50 diverse languages
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials.
On Event Individuation for Document-Level Information Extraction
As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE.
Iterative Document-level Information Extraction via Imitation Learning
We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i. e., N-tuples representing a mapping from named slots to spans of text) within a document.
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English.
Joint Universal Syntactic and Semantic Parsing
While numerous attempts have been made to jointly parse syntax and semantics, high performance in one domain typically comes at the price of performance in the other.
Decomposing and Recomposing Event Structure
We present an event structure classification empirically derived from inferential properties annotated on sentence- and document-level Universal Decompositional Semantics (UDS) graphs.
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain.
LOME: Large Ontology Multilingual Extraction
We present LOME, a system for performing multilingual information extraction.
Temporal Reasoning in Natural Language Inference
We introduce five new natural language inference (NLI) datasets focused on temporal reasoning.
Natural Language Inference with Mixed Effects
There is growing evidence that the prevalence of disagreement in the raw annotations used to construct natural language inference datasets makes the common practice of aggregating those annotations to a single label problematic.
Montague Grammar Induction
We propose a computational modeling framework for inducing combinatory categorial grammars from arbitrary behavioral data.
Frequency, Acceptability, and Selection: A case study of clause-embedding
We investigate the relationship between the frequency with which verbs are found in particular subcategorization frames and the acceptability of those verbs in those frames, focusing in particular on subordinate clause-taking verbs, such as "think", "want", and "tell".
Reading the Manual: Event Extraction as Definition Comprehension
We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals.
Universal Decompositional Semantic Parsing
We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores.
The Universal Decompositional Semantics Dataset and Decomp Toolkit
We present the Universal Decompositional Semantics (UDS) dataset (v1. 0), which is bundled with the Decomp toolkit (v0. 1).
The lexical and grammatical sources of neg-raising inferences
We investigate neg(ation)-raising inferences, wherein negation on a predicate can be interpreted as though in that predicate's subordinate clause.
A Framework for Decoding Event-Related Potentials from Text
We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model.
Fine-Grained Temporal Relation Extraction
We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales.
Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements
We present a novel semantic framework for modeling linguistic expressions of generalization---generic, habitual, and episodic statements---as combinations of simple, real-valued referential properties of predicates and their arguments.
Lexicosyntactic Inference in Neural Models
We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning.
Neural models of factuality
We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME.
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework
We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE).
The Semantic Proto-Role Linking Model
We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood judgments.
Computational linking theory
A linking theory explains how verbs' semantic arguments are mapped to their syntactic arguments---the inverse of the Semantic Role Labeling task from the shallow semantic parsing literature.
