They propose a neural architecture for assigning fine-grained labels to detected entity types. The model combines bidirectional LSTMs, attention over the context sequence, hand-engineered features, and the label hierarchy. They evaluate on Figer and OntoNotes datasets, showing improvements from each of the extensions.

https://i.imgur.com/HJL3CYy.png

They propose neural models for dialogue state tracking, making a binary decision for each possible slot-value pair, based on the latest context from the user and the system. The context utterances and the slot-value option are encoded into vectors, either by summing word representations or using a convnet. These vectors are then further combined to produce a binary output. The systems are evaluated on two dialogue datasets and show improvement over baselines that use hand-constructed lexicons.

https://i.imgur.com/G4rm954.png

The paper investigates better noising techniques for RNN language models.

https://i.imgur.com/cq5Kb0Y.png

A noising technique from previous work would be to randomly replace words in the context or replace them with a blank token. Here they investigate ways of choosing better which words to replace and choosing the replacements from a better distribution, inspired by methods in n-gram smoothing. They show improvement on language modeling (PTB and text8) and machine translation (English-German).

The paper investigates compositional semantic models specialised for metaphors.

https://i.imgur.com/OnoJK3h.png

They construct a dataset of 8592 adjective-noun phrases, covering 23 different adjectives, annotated for being metaphorical or literal. They then train compositional models to predict the phrase vector based on the noun vector, as a linear combination with an adjective-specific weight matrix. They show that it’s better to learn separate adjective matrices for literal and metaphorical uses of each adjective, even though the amount of training data is smaller.

The authors investigate the benefit of different task combinations when performing multi-task learning.

https://i.imgur.com/VmD2ioS.png

They experiment with all possible pairs of 10 sequence labeling datasets, switching between the datasets during training. They find that multi-task learning helps more when the main task quickly plateaus while the auxiliary task does not, likely helping the model out of local minima.
There does not seem to be any auxiliary task that would help on all main tasks, but chunking and semantic tagging seem to perform best.