15 citations · 32 across the 8 of their papers we have counts for
8 papers
WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in Wikipedia
Kenichiro Ando, Satoshi Sekine, Mamoru Komachi
Wikipedia can be edited by anyone and thus contains various quality sentences. Therefore, Wikipedia includes some poor-quality edits, which are often marked up by other editors. Wh…
Classifying Wikipedia in a fine-grained hierarchy: what graphs can contribute
Tiphaine Viard, Thomas McLachlan, Hamidreza Ghader +1
Wikipedia is a huge opportunity for machine learning, being the largest semi-structured base of knowledge available. Because of this, many works examine its contents, and focus on…
Select and Attend: Towards Controllable Content Selection in Text Generation
Xiaoyu Shen, Jun Suzuki, Kentaro Inui +3
Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box archit…
Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set
Hassan S. Shavarani, Satoshi Sekine
Wikipedia is a great source of general world knowledge which can guide NLP models better understand their motivation to make predictions. Structuring Wikipedia is the initial step…
Can neural networks understand monotonicity reasoning?
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +4
Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interactio…
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +4
Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI). Despite these efforts, neural models have a hard time cap…
Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition
Thai-Hoang Pham, Khai Mai, Nguyen Minh Trung +4
Fine-Grained Named Entity Recognition (FG-NER) is critical for many NLP applications. While classical named entity recognition (NER) has attracted a substantial amount of research,…
What Makes Reading Comprehension Questions Easier?
Saku Sugawara, Kentaro Inui, Satoshi Sekine +1
A challenge in creating a dataset for machine reading comprehension (MRC) is to collect questions that require a sophisticated understanding of language to answer beyond using supe…