83 citations · 124 across the 13 of their papers we have counts for
6 papers · 1 filter
Leveraging Dependency Forest for Neural Medical Relation Extraction
Linfeng Song, Yue Zhang, Daniel Gildea +3
Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source…
AMR-to-Text Generation with Cache Transition Systems
Lisa Jin, Daniel Gildea
Text generation from AMR involves emitting sentences that reflect the meaning of their AMR annotations. Neural sequence-to-sequence models have successfully been used to decode str…
SemBleu: A Robust Metric for AMR Parsing Evaluation
Linfeng Song, Daniel Gildea
Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major evaluation metric, SMATCH (Cai and Knight, 2013), searches for one-to-one mappings between the nod…
Predicting TED Talk Ratings from Language and Prosody
Md Iftekhar Tanveer, Md Kamrul Hassan, Daniel Gildea +1
We use the largest open repository of public speaking---TED Talks---to predict the ratings of the online viewers. Our dataset contains over 2200 TED Talk transcripts (includes over…
A Causality-Guided Prediction of the TED Talk Ratings from the Speech-Transcripts using Neural Networks
Md Iftekhar Tanveer, Md Kamrul Hasan, Daniel Gildea +1
Automated prediction of public speaking performance enables novel systems for tutoring public speaking skills. We use the largest open repository---TED Talks---to predict the ratin…
Semantic Neural Machine Translation using AMR
Linfeng Song, Daniel Gildea, Yue Zhang +2
It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many…