Empirical Methods in Natural Language Processing or EMNLP is a leading conference in the area of Natural Language Processing. EMNLP is organized by the ACL special interest group on linguistic data (SIGDAT).
Using error detection to improve error correction. A neural sequence labeling model is used to find correctness probabilities for every token, which are then used to rerank possible correction candidates. The process consistently improves the performance of different correction systems.
A specialised architecture for detecting metaphorical phrases. Uses a gating mechanism to condition one word based on the other, a neural version of weighted cosine similarity to make a prediction and hinge loss to optimise the model. Achieves high results on detecting metaphorical adjective-noun, verb-object and verb-subject phrases.
Active learning (choosing which examples to annotate for training) is proposed as a reinforcement learning problem. The Q-learning network predicts for each sentence whether it should be annotated, and is trained based on the performance improvement from the main task. Evaluation is done on NER, with experiments on transferring the trained Q-learning function to other languages.