Hopefully this can give you the general idea of 50 papers, in roughly 20 minutes of reading time.
They evaluate on the Co NLL-14 dataset, integrate probabilities from a large language model, and achieve good results. On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems Pei-Hao Su, Milica Gasic, Nikola Mrksic, Lina Rojas-Barahona, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, Steve Young. They train a supervised system which tries to predict the success on the current dialogue – if the model is certain about the outcome, the predicted label is used for training the dialogue system; if the model is uncertain, the user is asked to provide a label.
Essentially it reduces the amount of annotation that is required, by choosing which examples should be annotated through active learning. task is to predict feature norms – object properties, for example .
The numerical grounding helps quite a bit, and the best results are obtained when the KB conditioning is also added. Black Holes and White Rabbits : Metaphor Identification with Visual Features Ekaterina Shutova, Douwe Kiela, Jean Maillard. The basic system uses word embedding similarity – cosine between the word embeddings.
Then they explore variations using phrase embeddings, cos(phrase-word2, word2), which is similar to the operations with word regularities by Mikolov.