224 citations
- Microsoft (Canada)CA10 papers
- Microsoft (United States)US10 papers
- McGill UniversityCA4 papers
- Carnegie Mellon UniversityUS3 papers
- Georgia Institute of TechnologyUS3 papers
- Microsoft Research (United Kingdom)GB3 papers
- Université de MontréalCA3 papers
- Allen Institute for Artificial IntelligenceUS2 papers
- Boise State UniversityUS2 papers
- Centre Universitaire de MilaDZ2 papers
- Microsoft Research New York City (United States)2 papers
- Polytechnique MontréalCA2 papers
8 papers · 2 filters
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
Ali Emami, Paul Trichelair, Adam Trischler +3
We introduce a new benchmark for coreference resolution and NLI, Knowref, that targets common-sense understanding and world knowledge. Previous coreference resolution tasks can lar…
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
Yadollah Yaghoobzadeh, Hinrich Schütze
Knowledge bases (KBs) are paramount in NLP. We employ multiview learning for increasing accuracy and coverage of entity type information in KBs. We rely on two metaviews: language…
A Knowledge Hunting Framework for Common Sense Reasoning
Ali Emami, Noelia De La Cruz, Adam Trischler +2
We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms o…
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
Xingdi Yuan, Tong Wang, Rui Meng +4
Different texts shall by nature correspond to different number of keyphrases. This desideratum is largely missing from existing neural keyphrase generation models. In this study, w…
Lessons learned in multilingual grounded language learning
Ákos Kádár, Desmond Elliott, Marc-Alexandre Côté +2
Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions aff…
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Yikang Shen, Zhouhan Lin, Athul Paul Jacob +3
In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input se…