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Found 18 papers, 13 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.
Argument-Aware Approach To Event Linking
Event linking connects event mentions in text with relevant nodes in a knowledge base (KB).
TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction
In this work, we identify and address evaluation challenges, including inconsistency due to varying data assumptions or preprocessing steps, the insufficiency of current evaluation frameworks that may introduce dataset or data split bias, and the low reproducibility of some previous approaches.
Contextual Label Projection for Cross-Lingual Structured Prediction
Label projection, which involves obtaining translated labels and texts jointly, is essential for leveraging machine translation to facilitate cross-lingual transfer in structured prediction tasks.
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation
Paraphrase generation is a long-standing task in natural language processing (NLP).
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model
However, existing generation-based EAE models mostly focus on problem re-formulation and prompt design, without incorporating additional information that has been shown to be effective for classification-based models, such as the abstract meaning representation (AMR) of the input passages.
Code-Switched Text Synthesis in Unseen Language Pairs
In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training data.
Multi-hop Evidence Retrieval for Cross-document Relation Extraction
Relation Extraction (RE) has been extended to cross-document scenarios because many relations are not simply described in a single document.
GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles
We utilize this ontology to further introduce GENEVA, a diverse generalizability benchmarking dataset comprising four test suites, aimed at evaluating models' ability to handle limited data and unseen event type generalization.
TAGPRIME: A Unified Framework for Relational Structure Extraction
In this work, we propose to take a unified view of all these tasks and introduce TAGPRIME to address relational structure extraction problems.
Summarization as Indirect Supervision for Relation Extraction
Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i. e., extracting a kind of synoptical information that describes the relation of entity mentions.
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Relation Extraction
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Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction
We present a study on leveraging multilingual pre-trained generative language models for zero-shot cross-lingual event argument extraction (EAE).
DEGREE: A Data-Efficient Generation-Based Event Extraction Model
Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern.
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.
Discourse-level Relation Extraction via Graph Pooling
The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for discourse-level relation extraction (DRE) tasks.
Deep Structured Neural Network for Event Temporal Relation Extraction
We propose a novel deep structured learning framework for event temporal relation extraction.
NIESR: Nuisance Invariant End-to-end Speech Recognition
Deep neural network models for speech recognition have achieved great success recently, but they can learn incorrect associations between the target and nuisance factors of speech (e. g., speaker identities, background noise, etc.
Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-Sequence Model
We apply sequence-to-sequence model to mitigate the impact of speech recognition errors on open domain end-to-end dialog generation.
