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Found 11 papers, 1 papers with code
AutoEQA: Auto-Encoding Questions for Extractive Question Answering
There has been a significant progress in the field of Extractive Question Answering (EQA) in the recent years.
Investigating the Encoding of Words in BERT's Neurons using Feature Textualization
Pretrained language models (PLMs) form the basis of most state-of-the-art NLP technologies.
Linguistically inspired morphological inflection with a sequence to sequence model
Inflection is an essential part of every human language's morphology, yet little effort has been made to unify linguistic theory and computational methods in recent years.
CopyBERT: A Unified Approach to Question Generation with Self-Attention
Contextualized word embeddings provide better initialization for neural networks that deal with various natural language understanding (NLU) tasks including Question Answering (QA) and more recently, Question Generation(QG).
Wikinflection Corpus: A (Better) Multilingual, Morpheme-Annotated Inflectional Corpus
We are evaluating a generated, multilingual inflectional corpus with morpheme boundaries, generated from the English Wiktionary (Metheniti and Neumann, 2018), against the largest, multilingual, high-quality inflectional corpus of the UniMorph project (Kirov et al., 2018).
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task
This paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge.
DOMLIN at SemEval-2019 Task 8: Automated Fact Checking exploiting Ratings in Community Question Answering Forums
Our hand-in for subtask A consists of a fine-tuned classifier from this BERT checkpoint.
An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages
This paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets.
Neural Morphological Tagging from Characters for Morphologically Rich Languages
We systematically explore a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain character-based word vectors combined with bidirectional LSTMs to model across-word context in an end-to-end setting.
