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Found 14 papers, 4 papers with code
Do We Know What We Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0
Understanding when a text snippet does not provide a sought after information is an essential part of natural language utnderstanding.
BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language
Transformer-based language models have taken the NLP world by storm.
Capturing the Content of a Document through Complex Event Identification
We address this task as a pipeline, first predicting whether two granular events mentioned in the text belong to the same complex event, independently of their position in the text, and then using this to cluster them into complex events.
Yes, No or IDK: The Challenge of Unanswerable Yes/No Questions
For example, given the context “She married a lawyer from New-York.”, we don’t know whether the answer to the question “Did she marry in New York?” is “Yes” or “No”.
Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field.
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights
Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies.
Semantic Structural Decomposition for Neural Machine Translation
Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation.
The Language of Legal and Illegal Activity on the Darknet
The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity.
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results.
BLEU is Not Suitable for the Evaluation of Text Simplification
BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS).
Simple and Effective Text Simplification Using Semantic and Neural Methods
Here we present a simple and efficient splitting algorithm based on an automatic semantic parser.
Ranked #19 on
Text Simplification
on TurkCorpus
Semantic Structural Evaluation for Text Simplification
Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects.
SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation
Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general.
