12 citations · 60 across the 28 of their papers we have counts for
7 papers · 1 filter
Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization
Yilun Zhu, Siyao Peng, Sameer Pradhan +1
Previous attempts to incorporate a mention detection step into end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention span data as…
What's Hard in English RST Parsing? Predictive Models for Error Analysis
Yang Janet Liu, Tatsuya Aoyama, Amir Zeldes
Despite recent advances in Natural Language Processing (NLP), hierarchical discourse parsing in the framework of Rhetorical Structure Theory remains challenging, and our understand…
GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization
Yang Janet Liu, Amir Zeldes
Automatic summarization with pre-trained language models has led to impressively fluent results, but is prone to 'hallucinations', low performance on non-news genres, and outputs w…
GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation
Tatsuya Aoyama, Shabnam Behzad, Luke Gessler +6
We present GENTLE, a new mixed-genre English challenge corpus totaling 17K tokens and consisting of 8 unusual text types for out-of domain evaluation: dictionary entries, esports c…
Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity
Yang Janet Liu, Amir Zeldes
Recent advances in discourse parsing performance create the impression that, as in other NLP tasks, performance for high-resource languages such as English is finally becoming reli…
Are UD Treebanks Getting More Consistent? A Report Card for English UD
Amir Zeldes, Nathan Schneider
Recent efforts to consolidate guidelines and treebanks in the Universal Dependencies project raise the expectation that joint training and dataset comparison is increasingly possib…
MicroBERT: Effective Training of Low-resource Monolingual BERTs through Parameter Reduction and Multitask Learning
Luke Gessler, Amir Zeldes
Transformer language models (TLMs) are critical for most NLP tasks, but they are difficult to create for low-resource languages because of how much pretraining data they require. I…