In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Example Input: As our experience shows, humans can learn and deploy a myriad of different skills to tackle the situations they encounter daily. Neural networks, in contrast, have a fixed memory capacity that prevents them from learning more than a few sets of skills before starting to forget them.
In this work, we make a step to bridge neural networks with human-like learning capabilities. For this, we propose a model with a growing and open-bounded memory capacity that can be accessed based on the model's current demands. To test this system, we introduce a continual learning task based on language modelling where the model is exposed to multiple languages and domains in sequence, without providing any explicit signal on the type of input it is currently dealing with. The proposed system exhibits improved adaptation skills in that it can recover faster than comparable baselines after a switch in the input language or domain.
Example Output: We introduce a continual learning setup based on language modelling where no explicit task segmentation signal is given and propose a neural network model with growing long term memory to tackle it.

Example Input: Word alignments are useful for tasks like statistical and neural machine translation (NMT) and annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data and quality decreases as less training data is available. We propose word alignment methods that require little or no parallel data. The key idea is to leverage multilingual word embeddings both static and contextualized for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that traditional statistical aligners are outperformed by contextualized embeddings even in scenarios with abundant parallel data. For example, for a set of 100k parallel sentences, contextualized embeddings achieve a word alignment F1 that is more than 5% higher (absolute) than eflomal.
Example Output: We use representations trained without any parallel data for creating word alignments.

Example Input: High-dimensional data often lie in or close to low-dimensional subspaces. Sparse subspace clustering methods with sparsity induced by L0-norm, such as L0-Sparse Subspace Clustering (L0-SSC), are demonstrated to be more effective than its L1 counterpart such as Sparse Subspace Clustering (SSC). However, these L0-norm based subspace clustering methods are restricted to clean data that lie exactly in subspaces. Real data often suffer from noise and they may lie close to subspaces. We propose noisy L0-SSC to handle noisy data so as to improve the robustness. We show that the optimal solution to the optimization problem of noisy L0-SSC achieves subspace detection property (SDP), a key element with which data from different subspaces are separated, under deterministic and randomized models. Our results provide theoretical guarantee on the correctness of noisy L0-SSC in terms of SDP on noisy data. We further propose Noisy-DR-L0-SSC which provably recovers the subspaces on dimensionality reduced data. Noisy-DR-L0-SSC first projects the data onto a lower dimensional space by linear transformation, then performs noisy L0-SSC on the dimensionality reduced data so as to improve the efficiency. The experimental results demonstrate the effectiveness of noisy L0-SSC and Noisy-DR-L0-SSC.
Example Output:
We propose Noisy-DR-L0-SSC (Noisy Dimension Reduction L0-Sparse Subspace Clustering) to efficiently partition noisy data in accordance to their underlying subspace structure.