Besnik Fetahu

CycleNER: An Unsupervised Training Approach for Named Entity Recognition

2022 · Proceedings of the ACM Web Conference 2022

Named Entity Recognition (NER) is a crucial natural language understanding task for many down-stream tasks such as question answering and retrieval. Despite significant progress in developing NER models for multiple languages and domains, scaling to …

Reinforced Question Rewriting for Conversational Question Answering

2022 · arXiv (Cornell University)

Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conversational questions into self-contained ones is an emerging solution in industry settings …

Detecting Biased Statements in Wikipedia

2018

Quality in Wikipedia is enforced through a set of editing policies and guidelines recommended for Wikipedia editors. Neutral point of view (NPOV) is one of the main principles in Wikipedia, which ensures that for controversial …

MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition

2022 · arXiv (Cornell University)

We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed …