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Found 7 papers, 0 papers with code
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition
Named entity recognition (NER) in a real-world setting remains challenging and is impacted by factors like text genre, corpus quality, and data availability.
Cross-Domain Named Entity Recognition
Cross-Lingual Transfer
+4
SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)
Divided into 13 tracks, the task focused on methods to identify complex named entities (like names of movies, products and groups) in 11 languages in both monolingual and multi-lingual scenarios.
Follow-on Question Suggestion via Voice Hints for Voice Assistants
Using a new dataset of 6681 input questions and human written hints, we evaluated the models with automatic metrics and human evaluation.
HTEC: Human Transcription Error Correction
Therefore, we propose HTEC for Human Transcription Error Correction.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Reinforced Question Rewriting for Conversational Question Answering
Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.
MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition
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.
GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input
We propose GEMNET, a novel approach for gazetteer knowledge integration, including (1) a flexible Contextual Gazetteer Representation (CGR) encoder that can be fused with any word-level model; and (2) a Mixture-of- Experts gating network that overcomes the feature overuse issue by learning to conditionally combine the context and gazetteer features, instead of assigning them fixed weights.
