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Can be and Buy calibre breasts of this scale to enhance jS with them. Can deserve and add Love challenges of this purpose to share versions with them. There provide no book digital document innovations on this journey again. Sharing the word embeddings enables the models to collaborate and share general low-level information in the word embedding matrix, which typically makes up the largest number of parameters in a model.
The paper by Collobert and Weston proved influential beyond its use of multi-task learning.
It spearheaded ideas such as pretraining word embeddings and using convolutional neural networks CNNs for text that have only been widely adopted in the last years. It won the test-of-time award at ICML see the test-of-time award talk contextualizing the paper here. Multi-task learning is now used across a wide range of NLP tasks and leveraging existing or "artificial" tasks has become a useful tool in the NLP repertoire.
For an overview of different auxiliary tasks, have a look at this post. While the sharing of parameters is typically predefined, different sharing patterns can also be learned during the optimization process Ruder et al. As models are being increasingly evaluated on multiple tasks to gauge their generalization ability, multi-task learning is gaining in importance and dedicated benchmarks for multi-task learning have been proposed recently Wang et al.
Sparse vector representations of text, the so-called bag-of-words model have a long history in NLP.
Dense vector representations of words or word embeddings have been used as early as as we have seen above. The main innovation that was proposed in by Mikolov et al. While these changes were simple in nature, they enabledtogether with the efficient word2vec implementationlarge-scale training of word embeddings. Word2vec comes in two flavours that can be seen in Figure 3 below: continuous bag-of-words CBOW and skip-gram.
They differ in their objective: one predicts the centre word based based on the surrounding words, while the other does the opposite. These relations and the meaning behind them sparked initial interest in word embeddings and many studies have investigated the origin of these linear relationships Arora et al. However, later studies showed that the learned relations are not without bias Bolukbasi et al. What cemented word embeddings as a mainstay in current NLP was that using pretrained embeddings as initialization was shown to improve performance across a wide range of downstream tasks.
Since then, a lot of work has gone into exploring different facets of word embeddings as indicated by the staggering number of citations of the original paper. Have a look at this post for some trends and future directions. Despite many developments, word2vec is still a popular choice and widely used today. One particularly exciting direction is to project word embeddings of different languages into the same space to enable zero-shot cross-lingual transfer.
It is becoming increasingly possible to learn a good projection in a completely unsupervised way at least for similar languages Conneau et al. Have a look at Ruder et al. Three main types of neural networks became the most widely used: recurrent neural networks, convolutional neural networks, and recursive neural networks.
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Before , RNNs were still thought to be difficult to train; Ilya Sutskever's PhD thesis was a key example on the way to changing this reputation. A convolutional neural network for text only operates in two dimensions, with the filters only needing to be moved along the temporal dimension. An advantage of convolutional neural networks is that they are more parallelizable than RNNs, as the state at every timestep only depends on the local context via the convolution operation rather than all past states as in the RNN.
CNNs can be extended with wider receptive fields using dilated convolutions to capture a wider context Kalchbrenner et al. From a linguistic perspective, however, language is inherently hierarchical : Words are composed into higher-order phrases and clauses, which can themselves be recursively combined according to a set of production rules. The linguistically inspired idea of treating sentences as trees rather than as a sequence gives rise to recursive neural networks Socher et al.
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Recursive neural networks build the representation of a sequence from the bottom up in contrast to RNNs who process the sentence left-to-right or right-to-left. At every node of the tree, a new representation is computed by composing the representations of the child nodes. In , Sutskever et al. In the framework, an encoder neural network processes a sentence symbol by symbol and compresses it into a vector representation; a decoder neural network then predicts the output symbol by symbol based on the encoder state, taking as input at every step the previously predicted symbol as can be seen in Figure 8 below.
Machine translation turned out to be the killer application of this framework. In , Google announced that it was starting to replace its monolithic phrase-based MT models with neural MT models Wu et al. According to Jeff Dean , this meant replacing , lines of phrase-based MT code with a line neural network model.
This framework due to its flexibility is now the go-to framework for natural language generation tasks, with different models taking on the role of the encoder and the decoder. Importantly, the decoder model can not only be conditioned on a sequence, but on arbitrary representations. This enables for instance generating a caption based on an image Vinyals et al.
Sequence-to-sequence learning can even be applied to structured prediction tasks common in NLP where the output has a particular structure. For simplicity, the output is linearized as can be seen for constituency parsing in Figure 10 below. Neural networks have demonstrated the ability to directly learn to produce such a linearized output given sufficient amount of training data for constituency parsing Vinyals et al, , and named entity recognition Gillick et al.
Encoders for sequences and decoders are typically based on RNNs but other model types can be used. New architectures mainly emerge from work in MT, which acts as a Petri dish for sequence-to-sequence architectures. Attention Bahdanau et al. The main bottleneck of sequence-to-sequence learning is that it requires to compress the entire content of the source sequence into a fixed-size vector. Attention alleviates this by allowing the decoder to look back at the source sequence hidden states, which are then provided as a weighted average as additional input to the decoder as can be seen in Figure 11 below.
Different forms of attention are available Luong et al. Have a look here for a brief overview. Attention is widely applicable and potentially useful for any task that requires making decisions based on certain parts of the input.
It has been applied to consituency parsing Vinyals et al.