Search Results for author:
Found 17 papers, 5 papers with code
Keynote Abstract: Events on a Global Scale: Towards Language-Agnostic Event Extraction
I will compare them with approaches based on machine translation (as well as with models trained using in-language training data, where available), and discuss their strengths and weaknesses in different contexts, including the amount of English/foreign bitext available and the nature of the target event ontology.
Separating Style from Substance: Enhancing Cross-Genre Authorship Attribution through Data Selection and Presentation
The task of deciding whether two documents are written by the same author is challenging for both machines and humans.
Granting GPT-4 License and Opportunity: Enhancing Accuracy and Confidence Estimation for Few-Shot Event Detection
Large Language Models (LLMs) such as GPT-4 have shown enough promise in the few-shot learning context to suggest use in the generation of "silver" data and refinement of new ontologies through iterative application and review.
CAVE: Controllable Authorship Verification Explanations
Authorship Verification (AV) (do two documents have the same author?)
Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search
In this paper, we present ISI-Clear, a state-of-the-art, cross-lingual, zero-shot event extraction system and accompanying user interface for event visualization & search.
Impact of Subword Pooling Strategy on Cross-lingual Event Detection
Taking the task of cross-lingual event detection as a motivating example, we show that the choice of pooling strategy can have a significant impact on the target language performance.
Language Model Priming for Cross-Lingual Event Extraction
We show that by enabling the language model to better compensate for the deficits of sparse and noisy training data, our approach improves both trigger and argument detection and classification significantly over the state of the art in a zero-shot cross-lingual setting.
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations.
Low Resource Named Entity Recognition
named-entity-recognition
+2
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.
Teaching Machine Comprehension with Compositional Explanations
Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples.
SEARCHER: Shared Embedding Architecture for Effective Retrieval
We describe an approach to cross lingual information retrieval that does not rely on explicit translation of either document or query terms.
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation
Successfully training a deep neural network demands a huge corpus of labeled data.
Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation
Named entity recognition (NER) identifies typed entity mentions in raw text.
Ranked #11 on
Named Entity Recognition (NER)
on NCBI-disease
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage
In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed.
Last Words: What Can Be Accomplished with the State of the Art in Information Extraction? A Personal View
Though information extraction (IE) research has more than a 25-year history, F1 scores remain low.
Learning to Translate for Multilingual Question Answering
In multilingual question answering, either the question needs to be translated into the document language, or vice versa.
